From 7b1853617dd345bdf87ffc696c9d4af76e7e2db6 Mon Sep 17 00:00:00 2001 From: husein zolkepli Date: Mon, 17 Aug 2020 00:06:21 +0800 Subject: [PATCH] added constituency parsing --- README-pypi.rst | 3 + README.rst | 3 + accuracy/constituency-accuracy.png | Bin 0 -> 98015 bytes accuracy/constituency-template.js | 31 + accuracy/models-accuracy.ipynb | 87 +- accuracy/models-accuracy.rst | 40 +- .../models-accuracy_91_0.png | Bin 0 -> 98015 bytes docs/Api.rst | 9 + docs/Constituency.rst | 9 + docs/Contributing.rst | 2 + docs/README.rst | 3 + docs/index.rst | 10 +- docs/load-clustering.rst | 96 +- docs/load-constituency.rst | 146 + .../load-constituency_12_0.png | Bin 0 -> 21161 bytes .../load-constituency_2_0.png | Bin 0 -> 203397 bytes docs/models-accuracy.rst | 40 +- .../models-accuracy_91_0.png | Bin 0 -> 98015 bytes docs/running-on-windows.ipynb | 24 +- docs/running-on-windows.rst | 19 +- example/clustering/load-clustering.ipynb | 2 +- .../{README.rst => load-clustering.rst} | 96 +- example/constituency/README.rst | 146 + example/constituency/load-constituency.ipynb | 259 + .../load-constituency_12_0.png | Bin 0 -> 21161 bytes .../load-constituency_2_0.png | Bin 0 -> 203397 bytes malaya/__init__.py | 3 +- malaya/constituency.py | 120 + malaya/dependency.py | 2 +- malaya/model/tf.py | 155 + malaya/path/__init__.py | 75 +- malaya/text/bpe.py | 108 + malaya/text/chart_decoder.py | 119 + malaya/text/chart_decoder.pyx | 102 + malaya/text/rules.py | 5 + malaya/text/trees.py | 190 + session/constituency/EVALB/COLLINS.prm | 66 + session/constituency/EVALB/LICENSE | 24 + session/constituency/EVALB/Makefile | 4 + session/constituency/EVALB/README | 300 + session/constituency/EVALB/bug/bug.gld | 5 + session/constituency/EVALB/bug/bug.rsl-new | 39 + session/constituency/EVALB/bug/bug.rsl-old | 45 + session/constituency/EVALB/bug/bug.tst | 5 + session/constituency/EVALB/evalb | Bin 0 -> 48160 bytes session/constituency/EVALB/evalb.c | 1537 + session/constituency/EVALB/new.prm | 87 + session/constituency/EVALB/nk.prm | 92 + session/constituency/EVALB/sample/sample.gld | 24 + session/constituency/EVALB/sample/sample.prm | 65 + session/constituency/EVALB/sample/sample.rsl | 56 + session/constituency/EVALB/sample/sample.tst | 24 + session/constituency/EVALB/tgrep_proc.prl | 9 + session/constituency/EVALB_SPMRL/Makefile | 65 + session/constituency/EVALB_SPMRL/README | 76 + session/constituency/EVALB_SPMRL/README.orig | 230 + session/constituency/EVALB_SPMRL/evalb.c | 1724 + session/constituency/EVALB_SPMRL/spmrl.prm | 91 + .../constituency/EVALB_SPMRL/spmrl_hebrew.prm | 118 + session/constituency/EXPERIMENTS.md | 104 + session/constituency/LICENSE | 23 + session/constituency/README.md | 37 + session/constituency/augmentation.ipynb | 29830 ++++++++++++++++ session/constituency/benepar/__init__.py | 8 + session/constituency/benepar/base_parser.py | 354 + .../constituency/benepar/bert_tokenization.py | 294 + .../constituency/benepar/chart_decoder.pyx | 102 + 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| 492 + .../constituency/predict-albert-tiny.ipynb | 499 + session/constituency/predict-bert-base.ipynb | 652 + .../predict.ipynb => predict-tiny-bert.ipynb} | 219 +- session/constituency/predict-xlnet-base.ipynb | 486 + session/constituency/setup.py | 51 + .../src/alxlnet-base/config-old.json | 10 + .../constituency/src/alxlnet-base/config.json | 29 + .../src/alxlnet-base/special_tokens_map.json | 1 + .../src/alxlnet-base/spiece.model | Bin 0 -> 778744 bytes .../src/alxlnet-base/tokenizer_config.json | 1 + session/constituency/src/chart_helper.pyx | 153 + session/constituency/src/evaluate.py | 115 + session/constituency/src/main.py | 612 + session/constituency/src/main_albert_base.py | 612 + session/constituency/src/main_albert_tiny.py | 612 + session/constituency/src/main_alxlnet_base.py | 612 + session/constituency/src/main_bert_base.py | 612 + session/constituency/src/main_tiny_bert.py | 612 + session/constituency/src/main_xlnet_base.py | 612 + session/constituency/src/modeling_alxlnet.py | 2015 ++ session/constituency/src/nkutil.py | 50 + .../{model.py => src/parse_nk.py} | 0 .../constituency/src/parse_nk_albert_base.py | 1216 + .../constituency/src/parse_nk_albert_tiny.py | 1216 + .../constituency/src/parse_nk_alxlnet_base.py | 1215 + .../constituency/src/parse_nk_bert_base.py | 1218 + .../constituency/src/parse_nk_tiny_bert.py | 1218 + .../constituency/src/parse_nk_xlnet_base.py | 1213 + session/constituency/src/transliterate.py | 161 + session/constituency/src/trees.py | 297 + session/constituency/src/trees_newline.py | 302 + session/constituency/src/viz.py | 52 + session/constituency/src/vocabulary.py | 44 + session/constituency/viz/attention.vg.json | 323 + session/constituency/vocab-albert-base.json | 1 + session/constituency/vocab-albert-tiny.json | 1 + session/constituency/vocab-tiny.json | 1 + session/constituency/vocab-xlnet-base.json | 1 + session/constituency/vocab.json | 1 + session/constituency/xlnet-config.json | 1 + session/constituency/xlnet.py | 337 + setup-gpu.py | 2 +- setup.py | 2 +- 130 files changed, 66267 insertions(+), 907 deletions(-) create mode 100644 accuracy/constituency-accuracy.png create mode 100644 accuracy/constituency-template.js create mode 100644 accuracy/models-accuracy_files/models-accuracy_91_0.png create mode 100644 docs/Constituency.rst create mode 100644 docs/load-constituency.rst create mode 100644 docs/load-constituency_files/load-constituency_12_0.png create mode 100644 docs/load-constituency_files/load-constituency_2_0.png create mode 100644 docs/models-accuracy_files/models-accuracy_91_0.png rename example/clustering/{README.rst => load-clustering.rst} (90%) create mode 100644 example/constituency/README.rst create mode 100644 example/constituency/load-constituency.ipynb create mode 100644 example/constituency/load-constituency_files/load-constituency_12_0.png create mode 100644 example/constituency/load-constituency_files/load-constituency_2_0.png create mode 100644 malaya/constituency.py create mode 100644 malaya/text/chart_decoder.py create mode 100644 malaya/text/chart_decoder.pyx create mode 100644 malaya/text/trees.py create mode 100644 session/constituency/EVALB/COLLINS.prm create mode 100644 session/constituency/EVALB/LICENSE create mode 100644 session/constituency/EVALB/Makefile create mode 100644 session/constituency/EVALB/README create mode 100644 session/constituency/EVALB/bug/bug.gld create mode 100644 session/constituency/EVALB/bug/bug.rsl-new create mode 100644 session/constituency/EVALB/bug/bug.rsl-old create mode 100644 session/constituency/EVALB/bug/bug.tst create mode 100755 session/constituency/EVALB/evalb create mode 100644 session/constituency/EVALB/evalb.c create mode 100644 session/constituency/EVALB/new.prm create mode 100644 session/constituency/EVALB/nk.prm create mode 100644 session/constituency/EVALB/sample/sample.gld create mode 100644 session/constituency/EVALB/sample/sample.prm create mode 100644 session/constituency/EVALB/sample/sample.rsl create mode 100644 session/constituency/EVALB/sample/sample.tst create mode 100644 session/constituency/EVALB/tgrep_proc.prl create mode 100644 session/constituency/EVALB_SPMRL/Makefile create mode 100644 session/constituency/EVALB_SPMRL/README create mode 100644 session/constituency/EVALB_SPMRL/README.orig create mode 100644 session/constituency/EVALB_SPMRL/evalb.c create mode 100644 session/constituency/EVALB_SPMRL/spmrl.prm create mode 100644 session/constituency/EVALB_SPMRL/spmrl_hebrew.prm create mode 100644 session/constituency/EXPERIMENTS.md create mode 100644 session/constituency/LICENSE create mode 100644 session/constituency/README.md create mode 100644 session/constituency/augmentation.ipynb create mode 100644 session/constituency/benepar/__init__.py create mode 100644 session/constituency/benepar/base_parser.py create mode 100644 session/constituency/benepar/bert_tokenization.py create mode 100644 session/constituency/benepar/chart_decoder.pyx create mode 100644 session/constituency/benepar/downloader.py create mode 100644 session/constituency/benepar/nltk_plugin.py create mode 100644 session/constituency/benepar/spacy_plugin.py create mode 100644 session/constituency/chart_decoder.pyx create mode 100644 session/constituency/chart_decoder_py.py create mode 100644 session/constituency/custom_modeling.py create mode 100644 session/constituency/download-data.ipynb delete mode 100644 session/constituency/english/README.md delete mode 100644 session/constituency/english/export-bert.ipynb create mode 100644 session/constituency/export-albert-base.ipynb create mode 100644 session/constituency/export-albert-tiny.ipynb create mode 100644 session/constituency/export-alxlnet-base.ipynb create mode 100644 session/constituency/export-bert-base.ipynb create mode 100644 session/constituency/export-tiny-bert.ipynb create mode 100644 session/constituency/export-xlnet-base.ipynb create mode 100644 session/constituency/export/0001-bilm-tf-changes-for-use-with-benepar.patch create mode 100644 session/constituency/export/export.py create mode 100644 session/constituency/export/export_bert.py create mode 100644 session/constituency/indon.txt create mode 100644 session/constituency/predict-albert-base.ipynb create mode 100644 session/constituency/predict-albert-tiny.ipynb create mode 100644 session/constituency/predict-bert-base.ipynb rename session/constituency/{english/predict.ipynb => predict-tiny-bert.ipynb} (57%) create mode 100644 session/constituency/predict-xlnet-base.ipynb create mode 100644 session/constituency/setup.py create mode 100644 session/constituency/src/alxlnet-base/config-old.json create mode 100644 session/constituency/src/alxlnet-base/config.json create mode 100644 session/constituency/src/alxlnet-base/special_tokens_map.json create mode 100644 session/constituency/src/alxlnet-base/spiece.model create mode 100644 session/constituency/src/alxlnet-base/tokenizer_config.json create mode 100644 session/constituency/src/chart_helper.pyx create mode 100644 session/constituency/src/evaluate.py create mode 100644 session/constituency/src/main.py create mode 100644 session/constituency/src/main_albert_base.py create mode 100644 session/constituency/src/main_albert_tiny.py create mode 100644 session/constituency/src/main_alxlnet_base.py create mode 100644 session/constituency/src/main_bert_base.py create mode 100644 session/constituency/src/main_tiny_bert.py create mode 100644 session/constituency/src/main_xlnet_base.py create mode 100644 session/constituency/src/modeling_alxlnet.py create mode 100644 session/constituency/src/nkutil.py rename session/constituency/{model.py => src/parse_nk.py} (100%) create mode 100644 session/constituency/src/parse_nk_albert_base.py create mode 100644 session/constituency/src/parse_nk_albert_tiny.py create mode 100644 session/constituency/src/parse_nk_alxlnet_base.py create mode 100644 session/constituency/src/parse_nk_bert_base.py create mode 100644 session/constituency/src/parse_nk_tiny_bert.py create mode 100644 session/constituency/src/parse_nk_xlnet_base.py create mode 100644 session/constituency/src/transliterate.py create mode 100644 session/constituency/src/trees.py create mode 100644 session/constituency/src/trees_newline.py create mode 100644 session/constituency/src/viz.py create mode 100644 session/constituency/src/vocabulary.py create mode 100644 session/constituency/viz/attention.vg.json create mode 100644 session/constituency/vocab-albert-base.json create mode 100644 session/constituency/vocab-albert-tiny.json create mode 100644 session/constituency/vocab-tiny.json create mode 100644 session/constituency/vocab-xlnet-base.json create mode 100644 session/constituency/vocab.json create mode 100644 session/constituency/xlnet-config.json create mode 100644 session/constituency/xlnet.py diff --git a/README-pypi.rst b/README-pypi.rst index e1406d00..7b45c00b 100644 --- a/README-pypi.rst +++ b/README-pypi.rst @@ -26,6 +26,9 @@ Features - **Augmentation** Augment any text using dictionary of synonym, Wordvector or Transformer-Bahasa. +- **Constituency Parsing** + + Transfer learning on BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa. - **Dependency Parsing** Transfer learning on BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa, ALXLNET-base-bahasa. diff --git a/README.rst b/README.rst index 78881f6c..a8b63ceb 100644 --- a/README.rst +++ b/README.rst @@ -46,6 +46,9 @@ Features - **Augmentation** Augment any text using dictionary of synonym, Wordvector or Transformer-Bahasa. +- **Constituency Parsing** + + Transfer learning on BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa. - **Dependency Parsing** Transfer 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[ "from IPython.core.display import Image, display\n", "\n", @@ -83,22 +68,7 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": { - "image/png": { - "width": 500 - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display(Image('dependency-accuracy.png', width=500))" ] @@ -442,22 +412,7 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": { - "image/png": { - "width": 500 - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from IPython.core.display import Image, display\n", "\n", @@ -2287,12 +2242,40 @@ "display(Image('msen-accuracy.png', width=500))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constituency Parsing\n", + "\n", + "Trained on 80% of dataset, tested on 20% of dataset. Link to download dataset available inside the notebooks. All training sessions stored in [session/constituency](https://github.com/huseinzol05/Malaya/tree/master/session/constituency)\n", + "\n", + "**Graph based on FScore**." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "width": 500 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "display(Image('constituency-accuracy.png', width=500))" + ] } ], "metadata": { diff --git a/accuracy/models-accuracy.rst b/accuracy/models-accuracy.rst index 9f413747..9763c917 100644 --- a/accuracy/models-accuracy.rst +++ b/accuracy/models-accuracy.rst @@ -1,5 +1,5 @@ -**All y-axis been distorted and this can cause misrepresents data and -incorrect conclusion.** +**All y-axis been distorted purposely and this can cause misrepresents +data and incorrect conclusion.** Abstractive Summarization ------------------------- @@ -16,12 +16,6 @@ dataset available inside the notebooks. All training sessions stored in display(Image('abstractive-accuracy.png', width=500)) - - -.. image:: models-accuracy_files/models-accuracy_2_0.png - :width: 500px - - Full score at ``malaya.summarization.abstractive._t5_availability``. .. code:: python @@ -54,12 +48,6 @@ dataset available inside the notebooks. All training sessions stored in display(Image('dependency-accuracy.png', width=500)) - - -.. image:: models-accuracy_files/models-accuracy_5_0.png - :width: 500px - - bert-base ^^^^^^^^^ @@ -375,12 +363,6 @@ dataset available inside the notebooks. All training sessions stored in display(Image('emotion-accuracy.png', width=500)) - - -.. image:: models-accuracy_files/models-accuracy_13_0.png - :width: 500px - - multinomial ^^^^^^^^^^^ @@ -1759,3 +1741,21 @@ dataset available inside the notebooks. All training sessions stored in :width: 500px +Constituency Parsing +-------------------- + +Trained on 80% of dataset, tested on 20% of dataset. Link to download +dataset available inside the notebooks. All training sessions stored in +`session/constituency `__ + +**Graph based on FScore**. + +.. code:: ipython3 + + display(Image('constituency-accuracy.png', width=500)) + + + +.. image:: models-accuracy_files/models-accuracy_91_0.png + :width: 500px + diff --git a/accuracy/models-accuracy_files/models-accuracy_91_0.png b/accuracy/models-accuracy_files/models-accuracy_91_0.png new file mode 100644 index 0000000000000000000000000000000000000000..1ebdb1d95ce011c410e7044eea28f4dbf174a2ba GIT binary patch literal 98015 zcmeFZcT`hb+b@cU6j4!7kd7iC0!Bo735tS^q98>|1O!xi4LxEU&RlNYuiL%fJMR7Cjx)}8WDI1PthMHx&wSeaJtbIAM{DPHp6zsW zbUV+TJ$;#uZX1D)j$uDDBlyHy^&FCpZpX!&8X9`%G&F?t+?;K1I@r+BoehpQVlvkM z#F=Dz`P_{l=Cdpn!3UWcp0J+^5fZwxJNgL&`}t6olXc;e86#Kq_H6S!V{~S(IjfNU z8S~j!y%ejHHa6yFbq^Zj_qKN#RU#{$2~kK*Hyw_}AC#82t=7yAHL7!Fk~TdQ#{7BSdfH@R-aD&85+VHr zW_aZJyj6bjJN1P9>FI2R=G8_RXL{GvvyJC?M)ru;Yq5PjxcB~pn{Jr?(_aobR`#lz zoIG3Z$V2yuB^zz0DnQA*QqarSYYsu$Hq5BU=?47!QuT>DtkJ_EN;zQJF1!gWw0)x73Z4 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--- a/docs/Contributing.rst +++ b/docs/Contributing.rst @@ -12,6 +12,8 @@ Report bugs through `Github issue`_. Please report relevant information and preferably code that exhibits the problem. +Do not try to email us about the issues, we will not respond to the emails, submit a proper Github issue. + Fix Bugs -------- diff --git a/docs/README.rst b/docs/README.rst index 78881f6c..a8b63ceb 100644 --- a/docs/README.rst +++ b/docs/README.rst @@ -46,6 +46,9 @@ Features - **Augmentation** Augment any text using dictionary of synonym, Wordvector or Transformer-Bahasa. +- **Constituency Parsing** + + Transfer learning on BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa. - **Dependency Parsing** Transfer learning on BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa, ALXLNET-base-bahasa. diff --git a/docs/index.rst b/docs/index.rst index 34cfa79d..3457b194 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -24,6 +24,7 @@ Contents: Deployment Api Windows + Contributing .. toctree:: :maxdepth: 2 @@ -69,10 +70,16 @@ Contents: :maxdepth: 2 :caption: Tagging Module - Dependency Entities Pos +.. toctree:: + :maxdepth: 2 + :caption: Parsing Module + + Constituency + Dependency + .. toctree:: :maxdepth: 2 :caption: Summarization Module @@ -105,7 +112,6 @@ Contents: :maxdepth: 2 :caption: Misc - Contributing Crawler Donation Translator-malaya diff --git a/docs/load-clustering.rst b/docs/load-clustering.rst index 206405eb..85994848 100644 --- a/docs/load-clustering.rst +++ b/docs/load-clustering.rst @@ -23,49 +23,59 @@ Generate scatter plot for unsupervised clustering ------------------------------------------------- -\```python - -def cluster_scatter( corpus, vectorizer, num_clusters = 5, titles = -None, colors = None, stemming = True, stop_words = None, cleaning = -simple_textcleaning, clustering = KMeans, decomposition = MDS, ngram = -(1, 3), figsize = (17, 9), batch_size = 20, ): """ plot scatter plot on -similar text clusters. - -:: - - Parameters - ---------- - - corpus: list - vectorizer: class - num_clusters: int, (default=5) - size of unsupervised clusters. - titles: list - list of titles, length must same with corpus. - colors: list - list of colors, length must same with num_clusters. - stemming: bool, (default=True) - If True, sastrawi_stemmer will apply. - stop_words: list, (default=None) - list of stop words to remove. If None, default is malaya.texts._text_functions.STOPWORDS - ngram: tuple, (default=(1,3)) - n-grams size to train a corpus. - cleaning: function, (default=simple_textcleaning) - function to clean the corpus. - batch_size: int, (default=10) - size of strings for each vectorization and attention. Only useful if use transformer vectorizer. - - Returns - ------- - dictionary: { - 'X': X, - 'Y': Y, - 'labels': clusters, - 'vector': transformed_text_clean, - 'titles': titles, - } - """ - ``` +.. code:: python + + + def cluster_scatter( + corpus, + vectorizer, + num_clusters = 5, + titles = None, + colors = None, + stemming = True, + stop_words = None, + cleaning = simple_textcleaning, + clustering = KMeans, + decomposition = MDS, + ngram = (1, 3), + figsize = (17, 9), + batch_size = 20, + ): + """ + plot scatter plot on similar text clusters. + + Parameters + ---------- + + corpus: list + vectorizer: class + num_clusters: int, (default=5) + size of unsupervised clusters. + titles: list + list of titles, length must same with corpus. + colors: list + list of colors, length must same with num_clusters. + stemming: bool, (default=True) + If True, sastrawi_stemmer will apply. + stop_words: list, (default=None) + list of stop words to remove. If None, default is malaya.texts._text_functions.STOPWORDS + ngram: tuple, (default=(1,3)) + n-grams size to train a corpus. + cleaning: function, (default=simple_textcleaning) + function to clean the corpus. + batch_size: int, (default=10) + size of strings for each vectorization and attention. Only useful if use transformer vectorizer. + + Returns + ------- + dictionary: { + 'X': X, + 'Y': Y, + 'labels': clusters, + 'vector': transformed_text_clean, + 'titles': titles, + } + """ .. code:: python diff --git a/docs/load-constituency.rst b/docs/load-constituency.rst new file mode 100644 index 00000000..fe562f8b --- /dev/null +++ b/docs/load-constituency.rst @@ -0,0 +1,146 @@ +.. code:: python + + %%time + + import malaya + + +.. parsed-literal:: + + CPU times: user 4.75 s, sys: 1.24 s, total: 5.99 s + Wall time: 7.27 s + + +what is constituency parsing +---------------------------- + +Assign a sentence into its own syntactic structure, defined by certain +standardization. For example, + +.. code:: python + + from IPython.core.display import Image, display + + display(Image('constituency.png', width=500)) + + + +.. image:: load-constituency_files/load-constituency_2_0.png + :width: 500px + + +Read more at Stanford notes, +https://web.stanford.edu/~jurafsky/slp3/13.pdf + +The context free grammar totally depends on language, so for Bahasa, we +follow https://github.com/famrashel/idn-treebank + +List available transformer Constituency models +---------------------------------------------- + +.. code:: python + + malaya.constituency.available_transformer() + + + + +.. parsed-literal:: + + {'bert': ['470.0 MB', + 'Recall: 78.96', + 'Precision: 81.78', + 'FScore: 80.35', + 'CompleteMatch: 10.37', + 'TaggingAccuracy: 91.59'], + 'tiny-bert': ['125 MB', + 'Recall: 74.89', + 'Precision: 78.79', + 'FScore: 76.79', + 'CompleteMatch: 9.01', + 'TaggingAccuracy: 91.17'], + 'albert': ['180.0 MB', + 'Recall: 77.57', + 'Precision: 80.50', + 'FScore: 79.01', + 'CompleteMatch: 5.77', + 'TaggingAccuracy: 90.30'], + 'tiny-albert': ['56.7 MB', + 'Recall: 67.21', + 'Precision: 74.89', + 'FScore: 70.84', + 'CompleteMatch: 2.11', + 'TaggingAccuracy: 87.75'], + 'xlnet': ['498.0 MB', + 'Recall: 80.65', + 'Precision: 82.22', + 'FScore: 81.43', + 'CompleteMatch: 11.08', + 'TaggingAccuracy: 92.12']} + + + +Make sure you can check accuracy chart from here first before select a +model, +https://malaya.readthedocs.io/en/latest/Accuracy.html#constituency-parsing + +The best model in term of accuracy is **XLNET**. + +.. code:: python + + string = 'Dr Mahathir menasihati mereka supaya berhenti berehat dan tidur sebentar sekiranya mengantuk ketika memandu.' + +Load xlnet constituency model +----------------------------- + +.. code:: python + + model = malaya.constituency.transformer(model = 'xlnet') + + +.. parsed-literal:: + + WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/function/__init__.py:73: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead. + + WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/function/__init__.py:75: The name tf.GraphDef is deprecated. Please use tf.compat.v1.GraphDef instead. + + WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/function/__init__.py:68: The name tf.InteractiveSession is deprecated. Please use tf.compat.v1.InteractiveSession instead. + + + +Parse into NLTK Tree +^^^^^^^^^^^^^^^^^^^^ + +Make sure you already installed ``nltk``, if not, simply, + +.. code:: bash + + pip install nltk + +We preferred to parse into NLTK tree, so we can play around with +children / subtrees. + +.. code:: python + + tree = model.parse_nltk_tree(string) + +.. code:: python + + tree + + + + +.. image:: load-constituency_files/load-constituency_12_0.png + + + +Parse into Tree +^^^^^^^^^^^^^^^ + +This is a simple Tree object defined at +`malaya.text.trees `__. + +.. code:: python + + tree = model.parse_tree(string) diff --git a/docs/load-constituency_files/load-constituency_12_0.png b/docs/load-constituency_files/load-constituency_12_0.png new file mode 100644 index 0000000000000000000000000000000000000000..e356c13dd066fc2353685dc3078839053fcb1e0c GIT binary patch literal 21161 zcmaHS1z6PGyX}aGs7Qz?NH@{~(%p@8Nl7C$Lw5?&-K8KPErWClNQXG!2tx=1A}t_Y zcaPuqKlh$S7T_r_XEeHgg0|L3`fr|x>$lQsv 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All training sessions stored in display(Image('abstractive-accuracy.png', width=500)) - - -.. image:: models-accuracy_files/models-accuracy_2_0.png - :width: 500px - - Full score at ``malaya.summarization.abstractive._t5_availability``. .. code:: python @@ -54,12 +48,6 @@ dataset available inside the notebooks. All training sessions stored in display(Image('dependency-accuracy.png', width=500)) - - -.. image:: models-accuracy_files/models-accuracy_5_0.png - :width: 500px - - bert-base ^^^^^^^^^ @@ -375,12 +363,6 @@ dataset available inside the notebooks. All training sessions stored in display(Image('emotion-accuracy.png', width=500)) - - -.. image:: models-accuracy_files/models-accuracy_13_0.png - :width: 500px - - multinomial ^^^^^^^^^^^ @@ -1759,3 +1741,21 @@ dataset available inside the notebooks. All training sessions stored in :width: 500px +Constituency Parsing +-------------------- + +Trained on 80% of dataset, tested on 20% of dataset. Link to download +dataset available inside the notebooks. 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users will break on this part, so we need +to install Malaya without +`YouTokenToMe `__. + +.. code:: bash + + pip install malaya --no-deps + pip install tensorflow==1.15 + pip install bert-tensorflow albert-tensorflow + +If we skipped `YouTokenToMe `__, +we not able to use languaged-detection module, +https://malaya.readthedocs.io/en/latest/Language.html + Unable to use any T5 models --------------------------- @@ -32,4 +50,3 @@ Lack development on Windows Malaya been tested on modern UNIX / LINUX systems that supported tensorflow binary. Windows support not our development path right now (we don’t have any Windows machine here). - diff --git a/example/clustering/load-clustering.ipynb b/example/clustering/load-clustering.ipynb index 0a1a2304..5c5809e8 100644 --- a/example/clustering/load-clustering.ipynb +++ b/example/clustering/load-clustering.ipynb @@ -92,7 +92,7 @@ " 'titles': titles,\n", " }\n", " \"\"\"\n", - " ```" + "```" ] }, { diff --git a/example/clustering/README.rst b/example/clustering/load-clustering.rst similarity index 90% rename from example/clustering/README.rst rename to example/clustering/load-clustering.rst index cb9bbdd3..e9f11047 100644 --- a/example/clustering/README.rst +++ b/example/clustering/load-clustering.rst @@ -23,49 +23,59 @@ Generate scatter plot for unsupervised clustering ------------------------------------------------- -\```python - -def cluster_scatter( corpus, vectorizer, num_clusters = 5, titles = -None, colors = None, stemming = True, stop_words = None, cleaning = -simple_textcleaning, clustering = KMeans, decomposition = MDS, ngram = -(1, 3), figsize = (17, 9), batch_size = 20, ): """ plot scatter plot on -similar text clusters. - -:: - - Parameters - ---------- - - corpus: list - vectorizer: class - num_clusters: int, (default=5) - size of unsupervised clusters. - titles: list - list of titles, length must same with corpus. - colors: list - list of colors, length must same with num_clusters. - stemming: bool, (default=True) - If True, sastrawi_stemmer will apply. - stop_words: list, (default=None) - list of stop words to remove. If None, default is malaya.texts._text_functions.STOPWORDS - ngram: tuple, (default=(1,3)) - n-grams size to train a corpus. - cleaning: function, (default=simple_textcleaning) - function to clean the corpus. - batch_size: int, (default=10) - size of strings for each vectorization and attention. Only useful if use transformer vectorizer. - - Returns - ------- - dictionary: { - 'X': X, - 'Y': Y, - 'labels': clusters, - 'vector': transformed_text_clean, - 'titles': titles, - } - """ - ``` +.. code:: python + + + def cluster_scatter( + corpus, + vectorizer, + num_clusters = 5, + titles = None, + colors = None, + stemming = True, + stop_words = None, + cleaning = simple_textcleaning, + clustering = KMeans, + decomposition = MDS, + ngram = (1, 3), + figsize = (17, 9), + batch_size = 20, + ): + """ + plot scatter plot on similar text clusters. + + Parameters + ---------- + + corpus: list + vectorizer: class + num_clusters: int, (default=5) + size of unsupervised clusters. + titles: list + list of titles, length must same with corpus. + colors: list + list of colors, length must same with num_clusters. + stemming: bool, (default=True) + If True, sastrawi_stemmer will apply. + stop_words: list, (default=None) + list of stop words to remove. If None, default is malaya.texts._text_functions.STOPWORDS + ngram: tuple, (default=(1,3)) + n-grams size to train a corpus. + cleaning: function, (default=simple_textcleaning) + function to clean the corpus. + batch_size: int, (default=10) + size of strings for each vectorization and attention. Only useful if use transformer vectorizer. + + Returns + ------- + dictionary: { + 'X': X, + 'Y': Y, + 'labels': clusters, + 'vector': transformed_text_clean, + 'titles': titles, + } + """ .. code:: ipython3 diff --git a/example/constituency/README.rst b/example/constituency/README.rst new file mode 100644 index 00000000..06a3f053 --- /dev/null +++ b/example/constituency/README.rst @@ -0,0 +1,146 @@ +.. code:: ipython3 + + %%time + + import malaya + + +.. parsed-literal:: + + CPU times: user 4.75 s, sys: 1.24 s, total: 5.99 s + Wall time: 7.27 s + + +what is constituency parsing +---------------------------- + +Assign a sentence into its own syntactic structure, defined by certain +standardization. For example, + +.. code:: ipython3 + + from IPython.core.display import Image, display + + display(Image('constituency.png', width=500)) + + + +.. image:: load-constituency_files/load-constituency_2_0.png + :width: 500px + + +Read more at Stanford notes, +https://web.stanford.edu/~jurafsky/slp3/13.pdf + +The context free grammar totally depends on language, so for Bahasa, we +follow https://github.com/famrashel/idn-treebank + +List available transformer Constituency models +---------------------------------------------- + +.. code:: ipython3 + + malaya.constituency.available_transformer() + + + + +.. parsed-literal:: + + {'bert': ['470.0 MB', + 'Recall: 78.96', + 'Precision: 81.78', + 'FScore: 80.35', + 'CompleteMatch: 10.37', + 'TaggingAccuracy: 91.59'], + 'tiny-bert': ['125 MB', + 'Recall: 74.89', + 'Precision: 78.79', + 'FScore: 76.79', + 'CompleteMatch: 9.01', + 'TaggingAccuracy: 91.17'], + 'albert': ['180.0 MB', + 'Recall: 77.57', + 'Precision: 80.50', + 'FScore: 79.01', + 'CompleteMatch: 5.77', + 'TaggingAccuracy: 90.30'], + 'tiny-albert': ['56.7 MB', + 'Recall: 67.21', + 'Precision: 74.89', + 'FScore: 70.84', + 'CompleteMatch: 2.11', + 'TaggingAccuracy: 87.75'], + 'xlnet': ['498.0 MB', + 'Recall: 80.65', + 'Precision: 82.22', + 'FScore: 81.43', + 'CompleteMatch: 11.08', + 'TaggingAccuracy: 92.12']} + + + +Make sure you can check accuracy chart from here first before select a +model, +https://malaya.readthedocs.io/en/latest/Accuracy.html#constituency-parsing + +The best model in term of accuracy is **XLNET**. + +.. code:: ipython3 + + string = 'Dr Mahathir menasihati mereka supaya berhenti berehat dan tidur sebentar sekiranya mengantuk ketika memandu.' + +Load xlnet constituency model +----------------------------- + +.. code:: ipython3 + + model = malaya.constituency.transformer(model = 'xlnet') + + +.. parsed-literal:: + + WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/function/__init__.py:73: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead. + + WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/function/__init__.py:75: The name tf.GraphDef is deprecated. Please use tf.compat.v1.GraphDef instead. + + WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/function/__init__.py:68: The name tf.InteractiveSession is deprecated. Please use tf.compat.v1.InteractiveSession instead. + + + +Parse into NLTK Tree +^^^^^^^^^^^^^^^^^^^^ + +Make sure you already installed ``nltk``, if not, simply, + +.. code:: bash + + pip install nltk + +We preferred to parse into NLTK tree, so we can play around with +children / subtrees. + +.. code:: ipython3 + + tree = model.parse_nltk_tree(string) + +.. code:: ipython3 + + tree + + + + +.. image:: load-constituency_files/load-constituency_12_0.png + + + +Parse into Tree +^^^^^^^^^^^^^^^ + +This is a simple Tree object defined at +`malaya.text.trees `__. + +.. code:: ipython3 + + tree = model.parse_tree(string) diff --git a/example/constituency/load-constituency.ipynb b/example/constituency/load-constituency.ipynb new file mode 100644 index 00000000..f52b91f1 --- /dev/null +++ b/example/constituency/load-constituency.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 4.75 s, sys: 1.24 s, total: 5.99 s\n", + "Wall time: 7.27 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "import malaya" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## what is constituency parsing\n", + "\n", + "Assign a sentence into its own syntactic structure, defined by certain standardization. For example," + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "width": 500 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.core.display import Image, display\n", + "\n", + "display(Image('constituency.png', width=500))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Read more at Stanford notes, https://web.stanford.edu/~jurafsky/slp3/13.pdf\n", + "\n", + "The context free grammar totally depends on language, so for Bahasa, we follow https://github.com/famrashel/idn-treebank" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## List available transformer Constituency models" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'bert': ['470.0 MB',\n", + " 'Recall: 78.96',\n", + " 'Precision: 81.78',\n", + " 'FScore: 80.35',\n", + " 'CompleteMatch: 10.37',\n", + " 'TaggingAccuracy: 91.59'],\n", + " 'tiny-bert': ['125 MB',\n", + " 'Recall: 74.89',\n", + " 'Precision: 78.79',\n", + " 'FScore: 76.79',\n", + " 'CompleteMatch: 9.01',\n", + " 'TaggingAccuracy: 91.17'],\n", + " 'albert': ['180.0 MB',\n", + " 'Recall: 77.57',\n", + " 'Precision: 80.50',\n", + " 'FScore: 79.01',\n", + " 'CompleteMatch: 5.77',\n", + " 'TaggingAccuracy: 90.30'],\n", + " 'tiny-albert': ['56.7 MB',\n", + " 'Recall: 67.21',\n", + " 'Precision: 74.89',\n", + " 'FScore: 70.84',\n", + " 'CompleteMatch: 2.11',\n", + " 'TaggingAccuracy: 87.75'],\n", + " 'xlnet': ['498.0 MB',\n", + " 'Recall: 80.65',\n", + " 'Precision: 82.22',\n", + " 'FScore: 81.43',\n", + " 'CompleteMatch: 11.08',\n", + " 'TaggingAccuracy: 92.12']}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "malaya.constituency.available_transformer()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make sure you can check accuracy chart from here first before select a model, https://malaya.readthedocs.io/en/latest/Accuracy.html#constituency-parsing\n", + "\n", + "The best model in term of accuracy is **XLNET**." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "string = 'Dr Mahathir menasihati mereka supaya berhenti berehat dan tidur sebentar sekiranya mengantuk ketika memandu.'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load xlnet constituency model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/function/__init__.py:73: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n", + "\n", + "WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/function/__init__.py:75: The name tf.GraphDef is deprecated. Please use tf.compat.v1.GraphDef instead.\n", + "\n", + "WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/function/__init__.py:68: The name tf.InteractiveSession is deprecated. Please use tf.compat.v1.InteractiveSession instead.\n", + "\n" + ] + } + ], + "source": [ + "model = malaya.constituency.transformer(model = 'xlnet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Parse into NLTK Tree\n", + "\n", + "Make sure you already installed `nltk`, if not, simply,\n", + "\n", + "```bash\n", + "pip install nltk\n", + "```\n", + "\n", + "We preferred to parse into NLTK tree, so we can play around with children / subtrees." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "tree = model.parse_nltk_tree(string)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "Tree('S', [Tree('NP-SBJ', [Tree('', ['Dr']), Tree('NNP', ['Mahathir'])]), Tree('VP', [Tree('NNP', ['menasihati']), Tree('NP', [Tree('VB', ['mereka'])]), Tree('VP', [Tree('PRP', ['supaya']), Tree('VP', [Tree('VP', [Tree('IN', ['berhenti']), Tree('VB', ['berehat'])]), Tree('VB', ['dan']), Tree('VP', [Tree('CC', ['tidur']), Tree('VB', ['sebentar']), Tree('SBAR', [Tree('IN', ['sekiranya']), Tree('FRAG', [Tree('NP', [Tree('IN', ['mengantuk']), Tree('VB', ['ketika'])])])])])])])]), Tree('NN', ['memandu.'])])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Parse into Tree\n", + "\n", + "This is a simple Tree object defined at [malaya.text.trees](https://github.com/huseinzol05/Malaya/blob/master/malaya/text/trees.py)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": 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z+vpvsRxfwRb{xd2+W&EXlk`30yco)Do}12UkqQT^!34ix_pa(eko~QYakXL})IZk^ z;ERL}X;Bo_o9d0BA`3Iv7Q^|!t`(GY#czj^3CLeEpW;qs0 zEiI;BWUoOxF%#(A-f{1I6S1cKH$bPh40=DEp3%_x}Kb CM^lvm literal 0 HcmV?d00001 diff --git a/malaya/__init__.py b/malaya/__init__.py index d4889f50..9eadf372 100644 --- a/malaya/__init__.py +++ b/malaya/__init__.py @@ -14,7 +14,7 @@ home = os.path.join(str(Path.home()), 'Malaya') version = '3.8' -bump_version = '3.8.0' +bump_version = '3.8.1' version_path = os.path.join(home, 'version') __version__ = bump_version path = os.path.dirname(__file__) @@ -197,6 +197,7 @@ def clear_session(model): from . import augmentation from . import cluster +from . import constituency from . import dependency from . import emotion from . import entity diff --git a/malaya/constituency.py b/malaya/constituency.py new file mode 100644 index 00000000..ede7a955 --- /dev/null +++ b/malaya/constituency.py @@ -0,0 +1,120 @@ +from malaya.function import check_file, load_graph, generate_session +from malaya.text.bpe import ( + sentencepiece_tokenizer_bert, + sentencepiece_tokenizer_xlnet, +) +from malaya.text.trees import tree_from_str +from malaya.path import PATH_CONSTITUENCY, S3_PATH_CONSTITUENCY +import json +from herpetologist import check_type + +_availability = { + 'bert': [ + '470.0 MB', + 'Recall: 78.96', + 'Precision: 81.78', + 'FScore: 80.35', + 'CompleteMatch: 10.37', + 'TaggingAccuracy: 91.59', + ], + 'tiny-bert': [ + '125 MB', + 'Recall: 74.89', + 'Precision: 78.79', + 'FScore: 76.79', + 'CompleteMatch: 9.01', + 'TaggingAccuracy: 91.17', + ], + 'albert': [ + '180.0 MB', + 'Recall: 77.57', + 'Precision: 80.50', + 'FScore: 79.01', + 'CompleteMatch: 5.77', + 'TaggingAccuracy: 90.30', + ], + 'tiny-albert': [ + '56.7 MB', + 'Recall: 67.21', + 'Precision: 74.89', + 'FScore: 70.84', + 'CompleteMatch: 2.11', + 'TaggingAccuracy: 87.75', + ], + 'xlnet': [ + '498.0 MB', + 'Recall: 80.65', + 'Precision: 82.22', + 'FScore: 81.43', + 'CompleteMatch: 11.08', + 'TaggingAccuracy: 92.12', + ], +} + + +def available_transformer(): + """ + List available transformer constituency parsing models. + """ + return _availability + + +@check_type +def transformer(model: str = 'xlnet', **kwargs): + """ + Load Transformer Constituency Parsing model, transfer learning Transformer + self attentive parsing. + + Parameters + ---------- + model : str, optional (default='bert') + Model architecture supported. Allowed values: + + * ``'bert'`` - BERT architecture from google. + * ``'tiny-bert'`` - BERT architecture from google with smaller parameters. + * ``'albert'`` - ALBERT architecture from google. + * ``'tiny-albert'`` - ALBERT architecture from google with smaller parameters. + * ``'xlnet'`` - XLNET architecture from google. + + Returns + ------- + result : malaya.model.tf.CONSTITUENCY class + """ + + model = model.lower() + if model not in _availability: + raise Exception( + 'model not supported, please check supported models from malaya.constituency.available_transformer()' + ) + + check_file(PATH_CONSTITUENCY[model], S3_PATH_CONSTITUENCY[model], **kwargs) + g = load_graph(PATH_CONSTITUENCY[model]['model'], **kwargs) + + with open(PATH_CONSTITUENCY[model]['dictionary']) as fopen: + dictionary = json.load(fopen) + + if model in ['bert', 'tiny-bert', 'albert', 'tiny-albert']: + + tokenizer = sentencepiece_tokenizer_bert( + PATH_CONSTITUENCY[model]['tokenizer'], + PATH_CONSTITUENCY[model]['vocab'], + ) + mode = 'bert' + + if model in ['xlnet']: + tokenizer = sentencepiece_tokenizer_xlnet( + PATH_CONSTITUENCY[model]['tokenizer'] + ) + mode = 'xlnet' + + from malaya.model.tf import CONSTITUENCY + + return CONSTITUENCY( + input_ids = g.get_tensor_by_name('import/input_ids:0'), + word_end_mask = g.get_tensor_by_name('import/word_end_mask:0'), + charts = g.get_tensor_by_name('import/charts:0'), + tags = g.get_tensor_by_name('import/tags:0'), + sess = generate_session(graph = g, **kwargs), + tokenizer = tokenizer, + dictionary = dictionary, + mode = mode, + ) diff --git a/malaya/dependency.py b/malaya/dependency.py index 590413e8..579e54da 100644 --- a/malaya/dependency.py +++ b/malaya/dependency.py @@ -142,7 +142,7 @@ def available_transformer(): @check_type def transformer(model: str = 'xlnet', **kwargs): """ - Load Transformer Entity Tagging model, transfer learning Transformer + biaffine attention. + Load Transformer Dependency Parsing model, transfer learning Transformer + biaffine attention. Parameters ---------- diff --git a/malaya/model/tf.py b/malaya/model/tf.py index 1166d498..ae04ec9b 100644 --- a/malaya/model/tf.py +++ b/malaya/model/tf.py @@ -9,6 +9,13 @@ translation_textcleaning, pad_sentence_batch, ) +from malaya.text.bpe import ( + constituency_bert, + constituency_xlnet, + PTB_TOKEN_ESCAPE, +) +from malaya.text import chart_decoder +from malaya.text.trees import tree_from_str from herpetologist import check_type from typing import List @@ -224,3 +231,151 @@ def translate(self, strings: List[str], beam_search: bool = True): result: List[str] """ return self._translate(strings, beam_search = beam_search) + + +class CONSTITUENCY: + def __init__( + self, + input_ids, + word_end_mask, + charts, + tags, + sess, + tokenizer, + dictionary, + mode, + ): + + self._input_ids = input_ids + self._word_end_mask = word_end_mask + self._charts = charts + self._tags = tags + self._sess = sess + self._tokenizer = tokenizer + self._LABEL_VOCAB = dictionary['label'] + self._TAG_VOCAB = dictionary['tag'] + self._mode = mode + + def _parse(self, string): + s = string.split() + sentences = [s] + if self._mode == 'bert': + i, m = constituency_bert(self._tokenizer, sentences) + elif self._mode == 'xlnet': + i, m = constituency_xlnet(self._tokenizer, sentences) + else: + raise Exception( + 'mode not supported, only supported `bert` or `xlnet`' + ) + charts_val, tags_val = self._sess.run( + (self._charts, self._tags), + {self._input_ids: i, self._word_end_mask: m}, + ) + for snum, sentence in enumerate(sentences): + chart_size = len(sentence) + 1 + chart = charts_val[snum, :chart_size, :chart_size, :] + return s, tags_val[0], chart_decoder.decode(chart) + + @check_type + def parse_nltk_tree(self, string: str): + + """ + Parse a string into NLTK Tree, to make it useful, make sure you already installed tktinker. + + Parameters + ---------- + string : str + + Returns + ------- + result: nltk.Tree object + """ + + try: + import nltk + from nltk import Tree + except: + raise Exception( + 'nltk not installed. Please install it and try again.' + ) + + sentence, tags, (score, p_i, p_j, p_label) = self._parse(string) + + idx_cell = [-1] + + def make_tree(): + idx_cell[0] += 1 + idx = idx_cell[0] + i, j, label_idx = p_i[idx], p_j[idx], p_label[idx] + label = self._LABEL_VOCAB[label_idx] + if (i + 1) >= j: + word = sentence[i] + tag = self._TAG_VOCAB[tags[i]] + tag = PTB_TOKEN_ESCAPE.get(tag, tag) + word = PTB_TOKEN_ESCAPE.get(word, word) + tree = Tree(tag, [word]) + for sublabel in label[::-1]: + tree = Tree(sublabel, [tree]) + return [tree] + else: + left_trees = make_tree() + right_trees = make_tree() + children = left_trees + right_trees + if label: + tree = Tree(label[-1], children) + for sublabel in reversed(label[:-1]): + tree = Tree(sublabel, [tree]) + return [tree] + else: + return children + + tree = make_tree()[0] + tree.score = score + return tree + + @check_type + def parse_tree(self, string): + + """ + Parse a string into string treebank format. + + Parameters + ---------- + string : str + + Returns + ------- + result: malaya.text.trees.InternalTreebankNode class + """ + + sentence, tags, (score, p_i, p_j, p_label) = self._parse(string) + + idx_cell = [-1] + + def make_str(): + idx_cell[0] += 1 + idx = idx_cell[0] + i, j, label_idx = p_i[idx], p_j[idx], p_label[idx] + label = self._LABEL_VOCAB[label_idx] + if (i + 1) >= j: + word = sentence[i] + tag = self._TAG_VOCAB[tags[i]] + tag = PTB_TOKEN_ESCAPE.get(tag, tag) + word = PTB_TOKEN_ESCAPE.get(word, word) + s = '({} {})'.format(tag, word) + else: + children = [] + while ( + (idx_cell[0] + 1) < len(p_i) + and i <= p_i[idx_cell[0] + 1] + and p_j[idx_cell[0] + 1] <= j + ): + children.append(make_str()) + + s = ' '.join(children) + + for sublabel in reversed(label): + s = '({} {})'.format(sublabel, s) + return s + + return tree_from_str(make_str()) diff --git a/malaya/path/__init__.py b/malaya/path/__init__.py index ce25d01c..09088931 100644 --- a/malaya/path/__init__.py +++ b/malaya/path/__init__.py @@ -20,6 +20,77 @@ } } +PATH_CONSTITUENCY = { + 'bert': { + 'model': home + '/constituency/bert/base/model.pb', + 'dictionary': home + '/constituency/bert/base/vocab.json', + 'vocab': home + '/bert/sp10m.cased.bert.vocab', + 'tokenizer': home + '/bert/sp10m.cased.bert.model', + 'version': 'v38', + }, + 'tiny-bert': { + 'model': home + '/constituency/bert/tiny/model.pb', + 'dictionary': home + '/constituency/bert/tiny/vocab.json', + 'vocab': home + '/bert/sp10m.cased.bert.vocab', + 'tokenizer': home + '/bert/sp10m.cased.bert.model', + 'version': 'v38', + }, + 'albert': { + 'model': home + '/constituency/albert/base/model.pb', + 'dictionary': home + '/constituency/albert/base/vocab.json', + 'vocab': home + '/bert/sp10m.cased.v10.vocab', + 'tokenizer': home + '/bert/sp10m.cased.v10.model', + 'version': 'v38', + }, + 'tiny-albert': { + 'model': home + '/constituency/albert/tiny/model.pb', + 'dictionary': home + '/constituency/albert/tiny/vocab.json', + 'vocab': home + '/bert/sp10m.cased.v10.vocab', + 'tokenizer': home + '/bert/sp10m.cased.v10.model', + 'version': 'v38', + }, + 'xlnet': { + 'model': home + '/constituency/xlnet/base/model.pb', + 'dictionary': home + '/constituency/xlnet/base/vocab.json', + 'vocab': home + '/bert/sp10m.cased.v9.vocab', + 'tokenizer': home + '/bert/sp10m.cased.v9.model', + 'version': 'v38', + }, +} + +S3_PATH_CONSTITUENCY = { + 'bert': { + 'model': 'v38/constituency/bert-base.pb', + 'dictionary': 'v38/constituency/vocab-bert-base.json', + 'vocab': 'tokenizer/sp10m.cased.bert.vocab', + 'tokenizer': 'tokenizer/sp10m.cased.bert.model', + }, + 'tiny-bert': { + 'model': 'v38/constituency/tiny-bert.pb', + 'dictionary': 'v38/constituency/vocab-tiny-bert.json', + 'vocab': 'tokenizer/sp10m.cased.bert.vocab', + 'tokenizer': 'tokenizer/sp10m.cased.bert.model', + }, + 'albert': { + 'model': 'v38/constituency/albert-base.pb', + 'dictionary': 'v38/constituency/vocab-albert-base.json', + 'vocab': 'tokenizer/sp10m.cased.v10.vocab', + 'tokenizer': 'tokenizer/sp10m.cased.v10.model', + }, + 'tiny-albert': { + 'model': 'v38/constituency/albert-tiny.pb', + 'dictionary': 'v38/constituency/vocab-albert-tiny.json', + 'vocab': 'tokenizer/sp10m.cased.v10.vocab', + 'tokenizer': 'tokenizer/sp10m.cased.v10.model', + }, + 'xlnet': { + 'model': 'v38/constituency/xlnet-base.pb', + 'dictionary': 'v38/constituency/vocab-xlnet-base.json', + 'vocab': 'tokenizer/sp10m.cased.v9.vocab', + 'tokenizer': 'tokenizer/sp10m.cased.v9.model', + }, +} + PATH_DEPENDENCY = { 'bert': { 'model': home + '/dependency/bert/base/model.pb', @@ -1133,7 +1204,7 @@ 'path': home + '/bert-model/base', 'directory': home + '/bert-model/base/bert-base-v3/', 'model': { - 'model': home + '/bert-model/tiny/bert-bahasa-base.tar.gz', + 'model': home + '/bert-model/base/bert-bahasa-base.tar.gz', 'version': 'v34', }, }, @@ -1141,7 +1212,7 @@ 'path': home + '/bert-model/tiny', 'directory': home + '/bert-model/tiny/tiny-bert-v1/', 'model': { - 'model': home + '/bert-model/base/tiny-bert-bahasa.tar.gz', + 'model': home + '/bert-model/tiny/tiny-bert-bahasa.tar.gz', 'version': 'v34', }, }, diff --git a/malaya/text/bpe.py b/malaya/text/bpe.py index b5150a8a..f613978b 100644 --- a/malaya/text/bpe.py +++ b/malaya/text/bpe.py @@ -14,6 +14,38 @@ 'xlnet': {'pad': '', 'cls': '', 'sep': ''}, } +BERT_TOKEN_MAPPING = { + '-LRB-': '(', + '-RRB-': ')', + '-LCB-': '{', + '-RCB-': '}', + '-LSB-': '[', + '-RSB-': ']', + '``': '"', + "''": '"', + '`': "'", + '«': '"', + '»': '"', + '‘': "'", + '’': "'", + '“': '"', + 'â€': '"', + '„': '"', + '‹': "'", + '›': "'", + '\u2013': '--', # en dash + '\u2014': '--', # em dash +} + +PTB_TOKEN_ESCAPE = { + '(': '-LRB-', + ')': '-RRB-', + '{': '-LCB-', + '}': '-RCB-', + '[': '-LSB-', + ']': '-RSB-', +} + class SentencePieceTokenizer: def __init__(self, v, sp_model): @@ -484,3 +516,79 @@ def load_yttm(path, id_mode = False): raise Exception( f"model corrupted due to some reasons, please run malaya.clear_cache('{path}') and try again" ) + + +def constituency_bert(tokenizer, sentences): + all_input_ids, all_word_end_mask = [], [] + + subword_max_len = 0 + for snum, sentence in enumerate(sentences): + tokens = [] + word_end_mask = [] + + tokens.append('[CLS]') + word_end_mask.append(1) + + cleaned_words = [] + for word in sentence: + word = BERT_TOKEN_MAPPING.get(word, word) + if word == "n't" and cleaned_words: + cleaned_words[-1] = cleaned_words[-1] + 'n' + word = "'t" + cleaned_words.append(word) + + for word in cleaned_words: + word_tokens = tokenizer.tokenize(word) + if not word_tokens: + word_tokens = ['[UNK]'] + for _ in range(len(word_tokens)): + word_end_mask.append(0) + word_end_mask[-1] = 1 + tokens.extend(word_tokens) + tokens.append('[SEP]') + word_end_mask.append(1) + + input_ids = tokenizer.convert_tokens_to_ids(tokens) + all_input_ids.append(input_ids) + all_word_end_mask.append(word_end_mask) + + return all_input_ids, all_word_end_mask + + +def constituency_xlnet(tokenizer, sentences): + all_input_ids, all_word_end_mask = [], [] + + subword_max_len = 0 + for snum, sentence in enumerate(sentences): + tokens = [] + word_end_mask = [] + + cleaned_words = [] + for word in sentence: + word = BERT_TOKEN_MAPPING.get(word, word) + if word == "n't" and cleaned_words: + cleaned_words[-1] = cleaned_words[-1] + 'n' + word = "'t" + cleaned_words.append(word) + + for word in cleaned_words: + word_tokens = encode_pieces( + tokenizer, word, return_unicode = False, sample = False + ) + if not word_tokens: + word_tokens = [''] + for _ in range(len(word_tokens)): + word_end_mask.append(0) + word_end_mask[-1] = 1 + tokens.extend(word_tokens) + + tokens.append('') + word_end_mask.append(1) + tokens.append('') + word_end_mask.append(1) + + input_ids = [tokenizer.PieceToId(i) for i in tokens] + all_input_ids.append(input_ids) + all_word_end_mask.append(word_end_mask) + + return all_input_ids, all_word_end_mask diff --git a/malaya/text/chart_decoder.py b/malaya/text/chart_decoder.py new file mode 100644 index 00000000..c3a512e8 --- /dev/null +++ b/malaya/text/chart_decoder.py @@ -0,0 +1,119 @@ +import numpy as np + + +def decode(label_scores_chart): + NEG_INF = -np.inf + sentence_len = label_scores_chart.shape[0] - 1 + + value_chart = np.zeros( + (sentence_len + 1, sentence_len + 1), dtype = np.float32 + ) + split_idx_chart = np.zeros( + (sentence_len + 1, sentence_len + 1), dtype = np.int32 + ) + best_label_chart = np.zeros( + (sentence_len + 1, sentence_len + 1), dtype = np.int32 + ) + + length = 0 + left = 0 + right = 0 + + label_score = 0.0 + argmax_label_index = 0 + + best_split = 0 + split_idx = 0 + split_val = 0.0 + max_split_val = 0.0 + + label_index_iter = 0 + + for length in range(1, sentence_len + 1): + for left in range(0, sentence_len + 1 - length): + right = left + length + + # We do argmax ourselves to make sure it compiles to pure C + if length < sentence_len: + argmax_label_index = 0 + else: + # Not-a-span label is not allowed at the root of the tree + argmax_label_index = 1 + + label_score = label_scores_chart[left, right, argmax_label_index] + for label_index_iter in range(1, label_scores_chart.shape[2]): + if ( + label_scores_chart[left, right, label_index_iter] + > label_score + ): + argmax_label_index = label_index_iter + label_score = label_scores_chart[ + left, right, label_index_iter + ] + best_label_chart[left, right] = argmax_label_index + + if length == 1: + value_chart[left, right] = label_score + continue + + best_split = left + 1 + split_val = NEG_INF + for split_idx in range(left + 1, right): + max_split_val = ( + value_chart[left, split_idx] + value_chart[split_idx, right] + ) + if max_split_val > split_val: + split_val = max_split_val + best_split = split_idx + + value_chart[left, right] = ( + label_score + + value_chart[left, best_split] + + value_chart[best_split, right] + ) + split_idx_chart[left, right] = best_split + + # Now we need to recover the tree by traversing the chart starting at the + # root. This iterative implementation is faster than any of my attempts to + # use helper functions and recursion + + # All fully binarized trees have the same number of nodes + num_tree_nodes = 2 * sentence_len - 1 + included_i = np.empty(num_tree_nodes, dtype = np.int32) + included_j = np.empty(num_tree_nodes, dtype = np.int32) + included_label = np.empty(num_tree_nodes, dtype = np.int32) + + idx = 0 + stack_idx = 1 + # technically, the maximum stack depth is smaller than this + stack_i = np.empty(num_tree_nodes + 5, dtype = np.int32) + stack_j = np.empty(num_tree_nodes + 5, dtype = np.int32) + stack_i[1] = 0 + stack_j[1] = sentence_len + + i, j, k = 0, 0, 0 + while stack_idx > 0: + i = stack_i[stack_idx] + j = stack_j[stack_idx] + stack_idx -= 1 + included_i[idx] = i + included_j[idx] = j + included_label[idx] = best_label_chart[i, j] + idx += 1 + if i + 1 < j: + k = split_idx_chart[i, j] + stack_idx += 1 + stack_i[stack_idx] = k + stack_j[stack_idx] = j + stack_idx += 1 + stack_i[stack_idx] = i + stack_j[stack_idx] = k + + score = value_chart[0, sentence_len] + + return ( + score, + included_i.astype(int), + included_j.astype(int), + included_label.astype(int), + ) diff --git a/malaya/text/chart_decoder.pyx b/malaya/text/chart_decoder.pyx new file mode 100644 index 00000000..999bfc35 --- /dev/null +++ b/malaya/text/chart_decoder.pyx @@ -0,0 +1,102 @@ +import numpy as np +cimport numpy as np +from numpy cimport ndarray +cimport cython + +ctypedef np.float32_t DTYPE_t + +# @cython.boundscheck(False) +def decode(np.ndarray[DTYPE_t, ndim=3] label_scores_chart): + cdef DTYPE_t NEG_INF = -np.inf + cdef int sentence_len = label_scores_chart.shape[0] - 1 + + cdef np.ndarray[DTYPE_t, ndim=2] value_chart = np.zeros((sentence_len+1, sentence_len+1), dtype=np.float32) + cdef np.ndarray[int, ndim=2] split_idx_chart = np.zeros((sentence_len+1, sentence_len+1), dtype=np.int32) + cdef np.ndarray[int, ndim=2] best_label_chart = np.zeros((sentence_len+1, sentence_len+1), dtype=np.int32) + + cdef int length + cdef int left + cdef int right + + cdef DTYPE_t label_score + cdef int argmax_label_index + + cdef int best_split + cdef int split_idx # Loop variable for splitting + cdef DTYPE_t split_val # best so far + cdef DTYPE_t max_split_val + + cdef int label_index_iter + + for length in range(1, sentence_len + 1): + for left in range(0, sentence_len + 1 - length): + right = left + length + + # We do argmax ourselves to make sure it compiles to pure C + if length < sentence_len: + argmax_label_index = 0 + else: + # Not-a-span label is not allowed at the root of the tree + argmax_label_index = 1 + + label_score = label_scores_chart[left, right, argmax_label_index] + for label_index_iter in range(1, label_scores_chart.shape[2]): + if label_scores_chart[left, right, label_index_iter] > label_score: + argmax_label_index = label_index_iter + label_score = label_scores_chart[left, right, label_index_iter] + best_label_chart[left, right] = argmax_label_index + + if length == 1: + value_chart[left, right] = label_score + continue + + best_split = left + 1 + split_val = NEG_INF + for split_idx in range(left + 1, right): + max_split_val = value_chart[left, split_idx] + value_chart[split_idx, right] + if max_split_val > split_val: + split_val = max_split_val + best_split = split_idx + + value_chart[left, right] = label_score + value_chart[left, best_split] + value_chart[best_split, right] + split_idx_chart[left, right] = best_split + + # Now we need to recover the tree by traversing the chart starting at the + # root. This iterative implementation is faster than any of my attempts to + # use helper functions and recursion + + # All fully binarized trees have the same number of nodes + cdef int num_tree_nodes = 2 * sentence_len - 1 + cdef np.ndarray[int, ndim=1] included_i = np.empty(num_tree_nodes, dtype=np.int32) + cdef np.ndarray[int, ndim=1] included_j = np.empty(num_tree_nodes, dtype=np.int32) + cdef np.ndarray[int, ndim=1] included_label = np.empty(num_tree_nodes, dtype=np.int32) + + cdef int idx = 0 + cdef int stack_idx = 1 + # technically, the maximum stack depth is smaller than this + cdef np.ndarray[int, ndim=1] stack_i = np.empty(num_tree_nodes + 5, dtype=np.int32) + cdef np.ndarray[int, ndim=1] stack_j = np.empty(num_tree_nodes + 5, dtype=np.int32) + stack_i[1] = 0 + stack_j[1] = sentence_len + + cdef int i, j, k + while stack_idx > 0: + i = stack_i[stack_idx] + j = stack_j[stack_idx] + stack_idx -= 1 + included_i[idx] = i + included_j[idx] = j + included_label[idx] = best_label_chart[i, j] + idx += 1 + if i + 1 < j: + k = split_idx_chart[i, j] + stack_idx += 1 + stack_i[stack_idx] = k + stack_j[stack_idx] = j + stack_idx += 1 + stack_i[stack_idx] = i + stack_j[stack_idx] = k + + cdef DTYPE_t score = value_chart[0, sentence_len] + + return score, included_i.astype(int), included_j.astype(int), included_label.astype(int) diff --git a/malaya/text/rules.py b/malaya/text/rules.py index 8bea7b98..79391d61 100644 --- a/malaya/text/rules.py +++ b/malaya/text/rules.py @@ -1055,9 +1055,14 @@ 'at first': 'pertama kalinya', 'je lah': 'sahaja lah', 'ja lah': 'sahaja lah', + 'seriously though': 'serius walaupun', + 'seriously thou': 'serius walaupun', } rules_normalizer = { + 'seriously': 'biar betul', + 'sriusly': 'biar betul', + 'siriusly': 'biar betul', 'lu': 'kamu', 'pon': 'pun', 'jela': 'sahaja lah', diff --git a/malaya/text/trees.py b/malaya/text/trees.py new file mode 100644 index 00000000..6be0cf42 --- /dev/null +++ b/malaya/text/trees.py @@ -0,0 +1,190 @@ +import collections.abc +import gzip + + +class TreebankNode(object): + pass + + +class InternalTreebankNode(TreebankNode): + def __init__(self, label, children): + assert isinstance(label, str) + self.label = label + + assert isinstance(children, collections.abc.Sequence) + assert all(isinstance(child, TreebankNode) for child in children) + assert children + self.children = tuple(children) + + def linearize(self): + return '({} {})'.format( + self.label, ' '.join(child.linearize() for child in self.children) + ) + + def leaves(self): + for child in self.children: + yield from child.leaves() + + def convert(self, index = 0, nocache = False): + tree = self + sublabels = [self.label] + + while len(tree.children) == 1 and isinstance( + tree.children[0], InternalTreebankNode + ): + tree = tree.children[0] + sublabels.append(tree.label) + + children = [] + for child in tree.children: + children.append(child.convert(index = index)) + index = children[-1].right + + return InternalParseNode(tuple(sublabels), children, nocache = nocache) + + +class LeafTreebankNode(TreebankNode): + def __init__(self, tag, word): + assert isinstance(tag, str) + self.tag = tag + + assert isinstance(word, str) + self.word = word + + def linearize(self): + return '({} {})'.format(self.tag, self.word) + + def leaves(self): + yield self + + def convert(self, index = 0): + return LeafParseNode(index, self.tag, self.word) + + +class ParseNode(object): + pass + + +class InternalParseNode(ParseNode): + def __init__(self, label, children, nocache = False): + assert isinstance(label, tuple) + assert all(isinstance(sublabel, str) for sublabel in label) + assert label + self.label = label + + assert isinstance(children, collections.abc.Sequence) + assert all(isinstance(child, ParseNode) for child in children) + assert children + assert len(children) > 1 or isinstance(children[0], LeafParseNode) + assert all( + left.right == right.left + for left, right in zip(children, children[1:]) + ) + self.children = tuple(children) + + self.left = children[0].left + self.right = children[-1].right + + self.nocache = nocache + + def leaves(self): + for child in self.children: + yield from child.leaves() + + def convert(self): + children = [child.convert() for child in self.children] + tree = InternalTreebankNode(self.label[-1], children) + for sublabel in reversed(self.label[:-1]): + tree = InternalTreebankNode(sublabel, [tree]) + return tree + + def enclosing(self, left, right): + assert self.left <= left < right <= self.right + for child in self.children: + if isinstance(child, LeafParseNode): + continue + if child.left <= left < right <= child.right: + return child.enclosing(left, right) + return self + + def oracle_label(self, left, right): + enclosing = self.enclosing(left, right) + if enclosing.left == left and enclosing.right == right: + return enclosing.label + return () + + def oracle_splits(self, left, right): + return [ + child.left + for child in self.enclosing(left, right).children + if left < child.left < right + ] + + +class LeafParseNode(ParseNode): + def __init__(self, index, tag, word): + assert isinstance(index, int) + assert index >= 0 + self.left = index + self.right = index + 1 + + assert isinstance(tag, str) + self.tag = tag + + assert isinstance(word, str) + self.word = word + + def leaves(self): + yield self + + def convert(self): + return LeafTreebankNode(self.tag, self.word) + + +def tree_from_str(treebank, strip_top = True, strip_spmrl_features = True): + # Features bounded by `##` may contain spaces, so if we strip the features + # we need to do so prior to tokenization + if strip_spmrl_features: + treebank = ''.join(treebank.split('##')[::2]) + + tokens = treebank.replace('(', ' ( ').replace(')', ' ) ').split() + + def helper(index): + trees = [] + + while index < len(tokens) and tokens[index] == '(': + paren_count = 0 + while tokens[index] == '(': + index += 1 + paren_count += 1 + + label = tokens[index] + index += 1 + + if tokens[index] == '(': + children, index = helper(index) + trees.append(InternalTreebankNode(label, children)) + else: + word = tokens[index] + index += 1 + trees.append(LeafTreebankNode(label, word)) + + while paren_count > 0: + assert tokens[index] == ')' + index += 1 + paren_count -= 1 + + return trees, index + + trees, index = helper(0) + assert index == len(tokens) + + if strip_top: + for i, tree in enumerate(trees): + if tree.label in ('TOP', 'ROOT'): + assert len(tree.children) == 1 + trees[i] = tree.children[0] + + assert len(trees) == 1 + + return trees[0] diff --git a/session/constituency/EVALB/COLLINS.prm b/session/constituency/EVALB/COLLINS.prm new file mode 100644 index 00000000..cb1a2ff0 --- /dev/null +++ b/session/constituency/EVALB/COLLINS.prm @@ -0,0 +1,66 @@ +##------------------------------------------## +## Debug mode ## +## 0: No debugging ## +## 1: print data for individual sentence ## +##------------------------------------------## +DEBUG 0 + +##------------------------------------------## +## MAX error ## +## Number of error to stop the process. ## +## This is useful if there could be ## +## tokanization error. ## +## The process will stop when this number## +## of errors are accumulated. ## +##------------------------------------------## +MAX_ERROR 10 + +##------------------------------------------## +## Cut-off length for statistics ## +## At the end of evaluation, the ## +## statistics for the senetnces of length## +## less than or equal to this number will## +## be shown, on top of the statistics ## +## for all the sentences ## +##------------------------------------------## +CUTOFF_LEN 40 + +##------------------------------------------## +## unlabeled or labeled bracketing ## +## 0: unlabeled bracketing ## +## 1: labeled bracketing ## +##------------------------------------------## +LABELED 1 + +##------------------------------------------## +## Delete labels ## +## list of labels to be ignored. ## +## If it is a pre-terminal label, delete ## +## the word along with the brackets. ## +## If it is a non-terminal label, just ## +## delete the brackets (don't delete ## +## deildrens). ## +##------------------------------------------## +DELETE_LABEL TOP +DELETE_LABEL -NONE- +DELETE_LABEL , +DELETE_LABEL : +DELETE_LABEL `` +DELETE_LABEL '' +DELETE_LABEL . + +##------------------------------------------## +## Delete labels for length calculation ## +## list of labels to be ignored for ## +## length calculation purpose ## +##------------------------------------------## +DELETE_LABEL_FOR_LENGTH -NONE- + +##------------------------------------------## +## Equivalent labels, words ## +## the pairs are considered equivalent ## +## This is non-directional. ## +##------------------------------------------## +EQ_LABEL ADVP PRT + +# EQ_WORD Example example diff --git a/session/constituency/EVALB/LICENSE b/session/constituency/EVALB/LICENSE new file mode 100644 index 00000000..68a49daa --- /dev/null +++ b/session/constituency/EVALB/LICENSE @@ -0,0 +1,24 @@ +This is free and unencumbered software released into the public domain. + +Anyone is free to copy, modify, publish, use, compile, sell, or +distribute this software, either in source code form or as a compiled +binary, for any purpose, commercial or non-commercial, and by any +means. + +In jurisdictions that recognize copyright laws, the author or authors +of this software dedicate any and all copyright interest in the +software to the public domain. We make this dedication for the benefit +of the public at large and to the detriment of our heirs and +successors. We intend this dedication to be an overt act of +relinquishment in perpetuity of all present and future rights to this +software under copyright law. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR +OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, +ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR +OTHER DEALINGS IN THE SOFTWARE. + +For more information, please refer to diff --git a/session/constituency/EVALB/Makefile b/session/constituency/EVALB/Makefile new file mode 100644 index 00000000..0fe4ada5 --- /dev/null +++ b/session/constituency/EVALB/Makefile @@ -0,0 +1,4 @@ +all: evalb + +evalb: evalb.c + gcc -Wall -g -o evalb evalb.c diff --git a/session/constituency/EVALB/README b/session/constituency/EVALB/README new file mode 100644 index 00000000..106e927e --- /dev/null +++ b/session/constituency/EVALB/README @@ -0,0 +1,300 @@ +################################################################# +# # +# Bug fix and additional functionality for evalb # +# # +# This updated version of evalb fixes a bug in which sentences # +# were incorrectly categorized as "length mismatch" when the # +# the parse output had certain mislabeled parts-of-speech. # +# # +# The bug was the result of evalb treating one of the tags (in # +# gold or test) as a label to be deleted (see sections [6],[7] # +# for details), but not the corresponding tag in the other. # +# This most often occurs with punctuation. See the subdir # +# "bug" for an example gld and tst file demonstating the bug, # +# as well as output of evalb with and without the bug fix. # +# # +# For the present version in case of length mismatch, the nodes # +# causing the imbalance are reinserted to resolve the miscount. # +# If the lengths of gold and test truly differ, the error is # +# still reported. The parameter file "new.prm" (derived from # +# COLLINS.prm) shows how to add new potential mislabelings for # +# quotes (",``,',`). # +# # +# I have preserved DJB's revision for modern compilers except # +# for the delcaration of "exit" which is provided by stdlib. # +# # +# Other changes: # +# # +# * output of F-Measure in addition to precision and recall # +# (I did not update the documention in section [4] for this) # +# # +# * more comprehensive DEBUG output that includes bracketing # +# information as evalb is processing each sentence # +# (useful in working through this, and peraps other bugs). # +# Use either the "-D" run-time switch or set DEBUG to 2 in # +# the parameter file. # +# # +# * added DELETE_LABEL lines in new.prm for S1 nodes produced # +# by the Charniak parser and "?", "!" punctuation produced by # +# the Bikel parser. # +# # +# # +# David Ellis (Brown) # +# # +# January.2006 # +################################################################# + +################################################################# +# # +# Update of evalb for modern compilers # +# # +# This is an updated version of evalb, for use with modern C # +# compilers. There are a few updates, each marked in the code: # +# # +# /* DJB: explanation of comment */ # +# # +# The updates are purely to help compilation with recent # +# versions of GCC (and other C compilers). There are *NO* other # +# changes to the algorithm itself. # +# # +# I have made these changes following recommendations from # +# users of the Corpora Mailing List, especially Peet Morris and # +# Ramon Ziai. # +# # +# David Brooks (Birmingham) # +# # +# September.2005 # +################################################################# + +################################################################# +# # +# README file for evalb # +# # +# Satoshi Sekine (NYU) # +# Mike Collins (UPenn) # +# # +# October.1997 # +################################################################# + +Contents of this README: + + [0] COPYRIGHT + [1] INTRODUCTION + [2] INSTALLATION AND RUN + [3] OPTIONS + [4] OUTPUT FORMAT FROM THE SCORER + [5] HOW TO CREATE A GOLDFILE FROM THE TREEBANK + [6] THE PARAMETER FILE + [7] MORE DETAILS ABOUT THE SCORING ALGORITHM + + +[0] COPYRIGHT + +The authors abandon the copyright of this program. Everyone is +permitted to copy and distribute the program or a portion of the program +with no charge and no restrictions unless it is harmful to someone. + +However, the authors are delightful for the user's kindness of proper +usage and letting the authors know bugs or problems. + +This software is provided "AS IS", and the authors make no warranties, +express or implied. + +To legally enforce the abandonment of copyright, this package is released +under the Unlicense (see LICENSE). + +[1] INTRODUCTION + +Evaluation of bracketing looks simple, but in fact, there are minor +differences from system to system. This is a program to parametarize +such minor differences and to give an informative result. + +"evalb" evaluates bracketing accuracy in a test-file against a gold-file. +It returns recall, precision, tagging accuracy. It uses an identical +algorithm to that used in (Collins ACL97). + + +[2] Installation and Run + +To compile the scorer, type + +> make + + +To run the scorer: + +> evalb -p Parameter_file Gold_file Test_file + + +For example to use the sample files: + +> evalb -p sample.prm sample.gld sample.tst + + + +[3] OPTIONS + +You can specify system parameters in the command line options. +Other options concerning to evaluation metrix should be specified +in parameter file, described later. + + -p param_file parameter file + -d debug mode + -e n number of error to kill (default=10) + -h help + + + +[4] OUTPUT FORMAT FROM THE SCORER + +The scorer gives individual scores for each sentence, for +example: + + Sent. Matched Bracket Cross Correct Tag + ID Len. Stat. Recal Prec. Bracket gold test Bracket Words Tags Accracy +============================================================================ + 1 8 0 100.00 100.00 5 5 5 0 6 5 83.33 + +At the end of the output the === Summary === section gives statistics +for all sentences, and for sentences <=40 words in length. The summary +contains the following information: + +i) Number of sentences -- total number of sentences. + +ii) Number of Error/Skip sentences -- should both be 0 if there is no + problem with the parsed/gold files. + +iii) Number of valid sentences = Number of sentences - Number of Error/Skip + sentences + +iv) Bracketing recall = (number of correct constituents) + ---------------------------------------- + (number of constituents in the goldfile) + +v) Bracketing precision = (number of correct constituents) + ---------------------------------------- + (number of constituents in the parsed file) + +vi) Complete match = percentaage of sentences where recall and precision are + both 100%. + +vii) Average crossing = (number of constituents crossing a goldfile constituen + ---------------------------------------------------- + (number of sentences) + +viii) No crossing = percentage of sentences which have 0 crossing brackets. + +ix) 2 or less crossing = percentage of sentences which have <=2 crossing brackets. + +x) Tagging accuracy = percentage of correct POS tags (but see [5].3 for exact + details of what is counted). + + + +[5] HOW TO CREATE A GOLDFILE FROM THE PENN TREEBANK + + +The gold and parsed files are in a format similar to this: + +(TOP (S (INTJ (RB No)) (, ,) (NP (PRP it)) (VP (VBD was) (RB n't) (NP (NNP Black) (NNP Monday))) (. .))) + +To create a gold file from the treebank: + +tgrep -wn '/.*/' | tgrep_proc.prl + +will produce a goldfile in the required format. ("tgrep -wn '/.*/'" prints +parse trees, "tgrep_process.prl" just skips blank lines). + +For example, to produce a goldfile for section 23 of the treebank: + +tgrep -wn '/.*/' | tail +90895 | tgrep_process.prl | sed 2416q > sec23.gold + + + +[6] THE PARAMETER (.prm) FILE + + +The .prm file sets options regarding the scoring method. COLLINS.prm gives +the same scoring behaviour as the scorer used in (Collins 97). The options +chosen were: + +1) LABELED 1 + +to give labelled precision/recall figures, i.e. a constituent must have the +same span *and* label as a constituent in the goldfile. + +2) DELETE_LABEL TOP + +Don't count the "TOP" label (which is always given in the output of tgrep) +when scoring. + +3) DELETE_LABEL -NONE- + +Remove traces (and all constituents which dominate nothing but traces) when +scoring. For example + +.... (VP (VBD reported) (SBAR (-NONE- 0) (S (-NONE- *T*-1)))) (. .))) + +would be processed to give + +.... (VP (VBD reported)) (. .))) + + +4) +DELETE_LABEL , -- for the purposes of scoring remove punctuation +DELETE_LABEL : +DELETE_LABEL `` +DELETE_LABEL '' +DELETE_LABEL . + +5) DELETE_LABEL_FOR_LENGTH -NONE- -- don't include traces when calculating + the length of a sentence (important + when classifying a sentence as <=40 + words or >40 words) + +6) EQ_LABEL ADVP PRT + +Count ADVP and PRT as being the same label when scoring. + + + + +[7] MORE DETAILS ABOUT THE SCORING ALGORITHM + + +1) The scorer initially processes the files to remove all nodes specified +by DELETE_LABEL in the .prm file. It also recursively removes nodes which +dominate nothing due to all their children being removed. For example, if +-NONE- is specified as a label to be deleted, + +.... (VP (VBD reported) (SBAR (-NONE- 0) (S (-NONE- *T*-1)))) (. .))) + +would be processed to give + +.... (VP (VBD reported)) (. .))) + +2) The scorer also removes all functional tags attached to non-terminals +(functional tags are prefixed with "-" or "=" in the treebank). For example +"NP-SBJ" is processed to give "NP", "NP=2" is changed to "NP". + + +3) Tagging accuracy counts tags for all words *except* any tags which are +deleted by a DELETE_LABEL specification in the .prm file. (For example, for +COLLINS.prm, punctuation tagged as "," ":" etc. would not be included). + +4) When calculating the length of a sentence, all words with POS tags not +included in the "DELETE_LABEL_FOR_LENGTH" list in the .prm file are +counted. (For COLLINS.prm, only "-NONE-" is specified in this list, so +traces are removed before calculating the length of the sentence). + +5) There are some subtleties in scoring when either the goldfile or parsed +file contains multiple constituents for the same span which have the same +non-terminal label. e.g. (NP (NP the man)) If the goldfile contains n +constituents for the same span, and the parsed file contains m constituents +with that nonterminal, the scorer works as follows: + +i) If m>n, then the precision is n/m, recall is 100% + +ii) If n>m, then the precision is 100%, recall is m/n. + +iii) If n==m, recall and precision are both 100%. diff --git a/session/constituency/EVALB/bug/bug.gld b/session/constituency/EVALB/bug/bug.gld new file mode 100644 index 00000000..288a2540 --- /dev/null +++ b/session/constituency/EVALB/bug/bug.gld @@ -0,0 +1,5 @@ +(TOP (S (NP-SBJ (DT The) (NN Thy-1) (NN gene) (NN promoter) ) (VP (VBZ resembles) (NP (DT a) (`` ") (JJ housekeeping) ('' ") (NN promoter) ) (PP (IN in) (SBAR (IN that) (S (NP-SBJ-68 (PRP it) ) (VP-COOD (VP (VBZ is) (ADJP-PRD (JJ located) (PP (IN within) (NP (DT a) (JJ methylation-free) (NN island) )))) (, ,) (VP (VBZ lacks) (NP (DT a) (JJ canonical) (NN TATA) (NN box) )) (, ,) (CC and) (VP (VBZ displays) (NP (NN heterogeneity) ) (PP (IN in) (NP (NP (DT the) (JJ 5'-end) (NNS termini) ) (PP (IN of) (NP (DT the) (NN mRNA) )))))))))) (. .) ) ) +(TOP (S (NP-SBJ (DT The) (JJ latter) (`` ") (NP (NP (JJ nuclear) (NN factor) ) (PP (IN for) (NP (VBN activated) (NN T) (NNS cells) ))) ('' ") ) (ADVP (RB likely) ) (VP (VBZ contributes) (PP (TO to) (NP (NP (DT the) (NN tissue) (NN specificity) ) (PP (IN of) (NP (NN IL-2) (NN gene) (NN expression) ))))) (. .) ) ) +(TOP (S (ADVP (RB Thus) ) (, ,) (NP-SBJ (PRP we) ) (VP (VBD postulated) (SBAR-COOD (SBAR (IN that) (S (NP-SBJ (NP (DT the) (JJ circadian) (NN modification) ) (PP (IN of) (NP (NN GR) ))) (VP (VBD was) (ADJP-PRD (JJ independent) (PP (IN of) (NP-COOD (NP (NP (DT the) (JJ diurnal) (NNS fluctuations) ) (PP (IN in) (NP (NN plasma) (NN cortisol) (NN level) ))) (CC or) (NP (NP (DT the) (JJ circadian) (NNS variations) ) (PP (IN in) (NP (JJ environmental) (NN lighting) ))))))))) (CC and) (SBAR (IN that) (S (NP-SBJ-79 (DT the) (NN rhythmicity) ) (VP (MD might) (VP (VB be) (VP (VBN regulated) (NP (-NONE- *-79) ) (PP (IN by) (NP-LGS (NP (DT the) (`` ') (JJ circadian) (NN pacemaker) ('' ') ) (ADJP (JJ located) (PP (IN in) (NP (DT the) (JJ human) (JJ basal) (NN brain) )))))))))))) (. .) ) ) +(TOP (S (NP-SBJ-70 (JJ Such) (NN transcription) (NNS factors) ) (VP (VBP play) (NP (DT a) (JJ key) (NN role) ) (PP (IN in) (NP (NP (DT the) (NN development) ) (PP (IN of) (NP (DT the) (JJ mature) (NN T-cell) (NN phenotype) )))) (PP (IN by) (S (NP-SBJ (-NONE- *-70) ) (VP (VBG functioning) (PP (IN as) (`` ') (NP (NP (JJ master) (NNS regulators) ) (PP (IN of) (NP (NN T-cell) (NN differentiation) ))) ('' ') ))))) (. .) ) ) +(TOP (S (NP-SBJ (NP (DT The) (NN conversion) ) (PP (IN of) (NP (DT the) (NN TCEd) )) (PP (TO to) (NP (DT a) (`` ') (JJ perfect) ('' ') (NN NF-kB) (NN binding) (NN site) ))) (VP-COOD (VP (VBZ leads) (PP (TO to) (NP-19 (NP (DT a) (JJR tighter) (NN binding) ) (PP (IN of) (NP (NN NF-kB) )) (PP (TO to) (NP (NN TCEd) (NN DNA) ))))) (CC and) (, ,) (VP (PP (IN as) (NP (DT a) (JJ functional) (NN consequence) )) (, ,) (PP (TO to) (NP=19 (NP (DT the) (NN activity) ) (PP (IN of) (NP (DT the) (`` ') (VBN converted) ('' ') (NN TCEd) (NNS motifs) )) (PP (IN in) (NP (NN HeLa) (NNS cells) )))))) (. .) ) ) diff --git a/session/constituency/EVALB/bug/bug.rsl-new b/session/constituency/EVALB/bug/bug.rsl-new new file mode 100644 index 00000000..4b143a2a --- /dev/null +++ b/session/constituency/EVALB/bug/bug.rsl-new @@ -0,0 +1,39 @@ +Sent. Matched Bracket Cross Correct Tag +ID Len. Stat. Recal Prec. Bracket gold test Bracket Words Tags Accracy +============================================================================ +1 37 0 77.27 65.38 17 22 26 5 34 27 79.41 +2 21 0 69.23 64.29 9 13 14 2 20 16 80.00 +3 47 0 80.00 82.35 28 35 34 4 44 40 90.91 +4 26 0 35.29 37.50 6 17 16 8 25 18 72.00 +5 44 0 42.31 33.33 11 26 33 17 38 28 73.68 +============================================================================ + 62.83 57.72 71 113 123 0 161 129 80.12 +=== Summary === + +-- All -- +Number of sentence = 5 +Number of Error sentence = 0 +Number of Skip sentence = 0 +Number of Valid sentence = 5 +Bracketing Recall = 62.83 +Bracketing Precision = 57.72 +Bracketing FMeasure = 60.17 +Complete match = 0.00 +Average crossing = 7.20 +No crossing = 0.00 +2 or less crossing = 20.00 +Tagging accuracy = 80.12 + +-- len<=40 -- +Number of sentence = 3 +Number of Error sentence = 0 +Number of Skip sentence = 0 +Number of Valid sentence = 3 +Bracketing Recall = 61.54 +Bracketing Precision = 57.14 +Bracketing FMeasure = 59.26 +Complete match = 0.00 +Average crossing = 5.00 +No crossing = 0.00 +2 or less crossing = 33.33 +Tagging accuracy = 77.22 diff --git a/session/constituency/EVALB/bug/bug.rsl-old b/session/constituency/EVALB/bug/bug.rsl-old new file mode 100644 index 00000000..3f10bc04 --- /dev/null +++ b/session/constituency/EVALB/bug/bug.rsl-old @@ -0,0 +1,45 @@ +Sent. Matched Bracket Cross Correct Tag +ID Len. Stat. Recal Prec. Bracket gold test Bracket Words Tags Accracy +============================================================================ +1 : Length unmatch (33|35) + 1 37 1 0.00 0.00 0 0 0 0 0 0 0.00 +2 : Length unmatch (19|21) + 2 21 1 0.00 0.00 0 0 0 0 0 0 0.00 +3 : Length unmatch (44|45) + 3 47 1 0.00 0.00 0 0 0 0 0 0 0.00 +4 : Length unmatch (24|26) + 4 26 1 0.00 0.00 0 0 0 0 0 0 0.00 +5 : Length unmatch (38|39) + 5 44 1 0.00 0.00 0 0 0 0 0 0 0.00 +============================================================================ + 0 0 0.00 + +=== Summary === + +-- All -- +Number of sentence = 5 +Number of Error sentence = 5 +Number of Skip sentence = 0 +Number of Valid sentence = 0 +Bracketing Recall = 0.00 +Bracketing Precision = 0.00 +Bracketing FMeasure = nan +Complete match = 0.00 +Average crossing = 0.00 +No crossing = 0.00 +2 or less crossing = 0.00 +Tagging accuracy = 0.00 + +-- len<=40 -- +Number of sentence = 3 +Number of Error sentence = 3 +Number of Skip sentence = 0 +Number of Valid sentence = 0 +Bracketing Recall = 0.00 +Bracketing Precision = 0.00 +Bracketing FMeasure = nan +Complete match = 0.00 +Average crossing = 0.00 +No crossing = 0.00 +2 or less crossing = 0.00 +Tagging accuracy = 0.00 diff --git a/session/constituency/EVALB/bug/bug.tst b/session/constituency/EVALB/bug/bug.tst new file mode 100644 index 00000000..d6b51942 --- /dev/null +++ b/session/constituency/EVALB/bug/bug.tst @@ -0,0 +1,5 @@ +(S1 (S (NP (DT The) (JJ Thy-1) (NN gene) (NN promoter)) (VP (VP (VBZ resembles) (NP (NP (DT a) (ADJP (CD ") (NN housekeeping)) (NN ") (NN promoter)) (SBAR (WHPP (IN in) (WHNP (WDT that))) (S (NP (PRP it)) (VP (VBZ is) (VP (VBN located) (PP (IN within) (NP (DT a) (JJ methylation-free) (NN island))))))))) (, ,) (VP (VBZ lacks) (NP (DT a) (JJ canonical) (NNP TATA) (NN box))) (, ,) (CC and) (VP (VBZ displays) (NP (NP (NN heterogeneity)) (PP (IN in) (NP (NP (DT the) (JJ 5'-end) (NNS termini)) (PP (IN of) (NP (DT the) (NN mRNA)))))))) (. .))) +(S1 (S (NP (NP (DT The) (JJ latter) (CD ") (JJ nuclear) (NN factor)) (PP (IN for) (NP (VBN activated) (NN T) (NNS cells)))) (VP (VBZ ") (ADJP (JJ likely) (S (VP (VBZ contributes) (PP (TO to) (NP (NP (DT the) (NN tissue) (NN specificity)) (PP (IN of) (NP (JJ IL-2) (NN gene) (NN expression))))))))) (. .))) +(S1 (S (ADVP (RB Thus)) (, ,) (NP (PRP we)) (VP (VBD postulated) (SBAR (SBAR (IN that) (S (NP (NP (DT the) (JJ circadian) (NN modification)) (PP (IN of) (NP (NNP GR)))) (VP (VBD was) (ADJP (JJ independent) (PP (IN of) (NP (DT the) (JJ diurnal) (NNS fluctuations)))) (PP (IN in) (NP (NP (NN plasma) (JJ cortisol) (NN level)) (CC or) (NP (NP (DT the) (JJ circadian) (NNS variations)) (PP (IN in) (NP (JJ environmental) (NN lighting))))))))) (CC and) (SBAR (IN that) (S (NP (DT the) (NN rhythmicity)) (VP (MD might) (VP (VB be) (VP (VBN regulated) (PP (IN by) (NP (DT the) ('' ') (NP (JJ circadian) (NN pacemaker) (POS ')) (VP (VBN located) (PP (IN in) (NP (DT the) (JJ human) (JJ basal) (NN brain))))))))))))) (. .))) +(S1 (S (NP (JJ Such) (NN transcription) (NNS factors)) (VP (VBP play) (NP (NP (DT a) (JJ key) (NN role)) (PP (IN in) (NP (NP (DT the) (NN development)) (PP (IN of) (NP (NP (DT the) (JJ mature) (JJ T-cell) (NN phenotype)) (PP (IN by) (NP (NP (NN functioning) (RB as) (POS ')) (NN master) (NNS regulators))))) (PP (IN of) (NP (JJ T-cell) (NN differentiation) (POS '))))))) (. .))) +(S1 (S (NP (NP (DT The) (NN conversion)) (PP (IN of) (NP (DT the)))) (VP (VBD TCEd) (PP (TO to) (NP (NP (DT a) ('' ') (JJ perfect) ('' ') (NN NF-kB)) (SBAR (S (NP (JJ binding) (NN site)) (VP (VBZ leads) (PP (TO to) (NP (NP (NP (DT a) (ADJP (RBR tighter) (JJ binding)) (PP (IN of) (NP (NP (NNS NF-kB)) (PP (PP (TO to) (NP (JJ TCEd) (NN DNA))) (CC and) (PP (, ,) (PP (IN as) (NP (DT a) (JJ functional) (NN consequence))) (, ,) (TO to) (NP (NP (DT the) (NN activity)) (PP (IN of) (NP (DT the)))))))) (POS ')) (JJ converted) ('' ') (JJ TCEd) (NNS motifs)) (PP (IN in) (NP (NNP HeLa) (NNS cells))))))))))) (. .))) diff --git 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HcmV?d00001 diff --git a/session/constituency/EVALB/evalb.c b/session/constituency/EVALB/evalb.c new file mode 100644 index 00000000..9a3be2de --- /dev/null +++ b/session/constituency/EVALB/evalb.c @@ -0,0 +1,1537 @@ +/*****************************************************************/ +/* evalb [-p param_file] [-dh] [-e n] gold-file test-file */ +/* */ +/* Evaluate bracketing in test-file against gold-file. */ +/* Return recall, precision, tagging accuracy. */ +/* */ +/*