{"payload":{"feedbackUrl":"https://github.com/orgs/community/discussions/53140","repo":{"id":801914647,"defaultBranch":"main","name":"hynlp-lib_nlp","ownerLogin":"derricka83","currentUserCanPush":false,"isFork":false,"isEmpty":false,"createdAt":"2024-05-17T06:59:33.000Z","ownerAvatar":"https://avatars.githubusercontent.com/u/118951059?v=4","public":true,"private":false,"isOrgOwned":false},"refInfo":{"name":"","listCacheKey":"v0:1715934536.0","currentOid":""},"activityList":{"items":[{"before":"81528d96cce141b8a84a69a8028d33c798319048","after":"3a1480f94ca32c0248e1185ee00b4395a68524e6","ref":"refs/heads/main","pushedAt":"2024-07-24T03:37:07.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"derricka83","name":"D Adkison","path":"/derricka83","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/118951059?s=80&v=4"},"commit":{"message":"Update README.md","shortMessageHtmlLink":"Update README.md"}},{"before":"423aace1e008f0bf70e3c09b2ed59b8a9d0ad7de","after":"81528d96cce141b8a84a69a8028d33c798319048","ref":"refs/heads/main","pushedAt":"2024-05-17T08:48:14.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"derricka83","name":"D Adkison","path":"/derricka83","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/118951059?s=80&v=4"},"commit":{"message":"Update shynlp_cc.ipynb\n\nThis commit adds the initial Jupyter notebook file (`hynlp_cc.ipynb`) for the **Core Concepts** section of the HYNLP documentation. The notebook includes detailed explanations and code examples covering theoretical foundations, quantum computing principles, hybrid NLP overview, quantum-inspired text encoding, and decoding. Visual aids and diagrams are also provided in the accompanying `diagrams` folder. Additionally, the `references` folder contains relevant academic papers and articles for further reading.\r\n\r\n```python\r\n# Example code block\r\ndef encode_text(text):\r\n    \"\"\"\r\n    Encodes the given text into a classical representation of a quantum circuit.\r\n    \"\"\"\r\n    # Preprocess text\r\n    text = re.sub(r'[^a-zA-Z\\s]', '', text.lower())\r\n    tokens = word_tokenize(text)\r\n    filtered_tokens = [token for token in tokens if token not in stopwords.words('english')]\r\n    stemmer = PorterStemmer()\r\n    stemmed_tokens = [stemmer.stem(token) for token in filtered_tokens]\r\n\r\n    # Create classical representation of quantum circuit\r\n    circuit = []\r\n\r\n    # Encode text into classical representation\r\n    for i, token in enumerate(stemmed_tokens):\r\n        circuit.append(('x', i))  # Simulate X gate\r\n        circuit.append(('qft', i))  # Simulate QFT gate\r\n\r\n    return circuit","shortMessageHtmlLink":"Update shynlp_cc.ipynb"}},{"before":"bf22a82de3bb22faba29e87175ea7f036827bb5e","after":"423aace1e008f0bf70e3c09b2ed59b8a9d0ad7de","ref":"refs/heads/main","pushedAt":"2024-05-17T08:46:29.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"derricka83","name":"D Adkison","path":"/derricka83","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/118951059?s=80&v=4"},"commit":{"message":"Update shynlp_cc.ipynb\n\n```python\r\n# HYNLP Core Concepts\r\n\r\n## Theoretical Foundations\r\n\r\nIn this section, we delve into the \r\ntheoretical foundations of hybrid \r\nnatural language processing (HYNLP). \r\nWe explore the underlying principles \r\nthat guide the integration of classical \r\nNLP techniques with quantum-inspired \r\napproaches.\r\n\r\n### Classical NLP Theory\r\n\r\nClassical NLP theory forms the basis for \r\nunderstanding traditional methods of natural \r\nlanguage processing. We cover concepts such \r\nas tokenization, part-of-speech tagging, \r\nand syntactic parsing.\r\n\r\n```python\r\ndef tokenize(text):\r\n    \"\"\"Tokenizes the input text.\"\"\"\r\n    return text.split()\r\n\r\ndef pos_tag(tokens):\r\n    \"\"\"Performs part-of-speech tagging on the input tokens.\"\"\"\r\n    # Implementation of POS tagging algorithm\r\n    pass\r\n\r\ndef syntactic_parse(sentence):\r\n    \"\"\"Performs syntactic parsing on the input sentence.\"\"\"\r\n    # Implementation of syntactic parsing algorithm\r\n    pass\r\n```\r\n\r\n### Quantum Computing Principles\r\n\r\nTo comprehend HYNLP fully, it's essential to have a basic \r\nunderstanding of quantum computing principles. We provide an \r\nintroductory overview of quantum mechanics, qubits, and quantum gates.\r\n\r\n```python\r\nfrom qiskit import QuantumCircuit, Aer, execute\r\n\r\n# Example quantum circuit to create a superposition state\r\nqc = QuantumCircuit(1, 1)\r\nqc.h(0)\r\nqc.measure(0, 0)\r\n\r\nbackend = Aer.get_backend('qasm_simulator')\r\njob = execute(qc, backend)\r\nresult = job.result()\r\ncounts = result.get_counts()\r\nprint(counts)\r\n```\r\n\r\n## Hybrid NLP Overview\r\n\r\nIn this section, we provide a comprehensive overview of \r\nhow HYNLP integrates classical and quantum approaches to \r\nnatural language processing. We discuss the synergy between \r\nclassical NLP techniques and quantum-inspired algorithms, \r\nhighlighting the advantages of hybrid models.\r\n\r\n### Quantum-Inspired Text Encoding\r\n\r\nOne key aspect of HYNLP is its ability to encode text into \r\nquantum-inspired formats. We demonstrate various encoding \r\nschemes and their applications in hybrid NLP models.\r\n\r\n```python\r\ndef encode_text(text):\r\n    \"\"\"Encodes the input text using quantum-inspired encoding.\"\"\"\r\n    # Implementation of text encoding algorithm\r\n    pass\r\n```\r\n\r\n### Decoding Quantum Representations\r\n\r\nOnce text is encoded into quantum-inspired formats, it needs to \r\nbe decoded back into classical representations. We explain the \r\ndecoding process and its significance in HYNLP.\r\n\r\n```python\r\ndef decode_text(encoded_text):\r\n    \"\"\"Decodes the input text from quantum-inspired format.\"\"\"\r\n    # Implementation of text decoding algorithm\r\n    pass\r\n```\r\n\r\n## Visual Aids and Diagrams\r\n\r\nThis folder contains visual aids and diagrams that illustrate key \r\nconcepts discussed in the theoretical foundations and hybrid NLP \r\noverview sections.\r\n\r\n## References\r\n\r\nThe references folder includes academic papers, articles, and other \r\nreference materials that provide background information and further \r\nreading on hybrid NLP and quantum computing.\r\n```","shortMessageHtmlLink":"Update shynlp_cc.ipynb"}},{"before":"250f0d0a6cf530aeac658d7b413beccda0fbf1e8","after":"bf22a82de3bb22faba29e87175ea7f036827bb5e","ref":"refs/heads/main","pushedAt":"2024-05-17T08:44:11.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"derricka83","name":"D Adkison","path":"/derricka83","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/118951059?s=80&v=4"},"commit":{"message":"Create shynlp_cc.ipynb\n\nFor the commit message, you can write:\r\n\r\n\"Add initial Jupyter notebook file for HYNLP Core Concepts\"\r\n\r\nThis commit adds the initial Jupyter notebook file (`hynlp_cc.ipynb`) for the **Core Concepts** section of the HYNLP documentation. The notebook includes detailed explanations and code examples covering theoretical foundations, quantum computing principles, hybrid NLP overview, quantum-inspired text encoding, and decoding. Visual aids and diagrams are also provided in the accompanying `diagrams` folder. Additionally, the `references` folder contains relevant academic papers and articles for further reading.\r\n\r\n```python\r\n# Example code block\r\ndef encode_text(text):\r\n    \"\"\"\r\n    Encodes the given text into a classical representation of a quantum circuit.\r\n    \"\"\"\r\n    # Preprocess text\r\n    text = re.sub(r'[^a-zA-Z\\s]', '', text.lower())\r\n    tokens = word_tokenize(text)\r\n    filtered_tokens = [token for token in tokens if token not in stopwords.words('english')]\r\n    stemmer = PorterStemmer()\r\n    stemmed_tokens = [stemmer.stem(token) for token in filtered_tokens]\r\n\r\n    # Create classical representation of quantum circuit\r\n    circuit = []\r\n\r\n    # Encode text into classical representation\r\n    for i, token in enumerate(stemmed_tokens):\r\n        circuit.append(('x', i))  # Simulate X gate\r\n        circuit.append(('qft', i))  # Simulate QFT gate\r\n\r\n    return circuit","shortMessageHtmlLink":"Create shynlp_cc.ipynb"}},{"before":"8f31e3cac0390bf9b05c948539cb248e28f6f4f8","after":null,"ref":"refs/heads/derricka83-feature/intro-to-hynlp-guide","pushedAt":"2024-05-17T08:28:56.000Z","pushType":"branch_deletion","commitsCount":0,"pusher":{"login":"derricka83","name":"D Adkison","path":"/derricka83","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/118951059?s=80&v=4"}},{"before":"acc8ad4f4ec7762092a8916f9f2097a0319730d0","after":"250f0d0a6cf530aeac658d7b413beccda0fbf1e8","ref":"refs/heads/main","pushedAt":"2024-05-17T08:28:41.000Z","pushType":"pr_merge","commitsCount":1,"pusher":{"login":"derricka83","name":"D Adkison","path":"/derricka83","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/118951059?s=80&v=4"},"commit":{"message":"Create Intro_to_hynlp.md (#1)\n\n**Commit message:** Create Intro_to_hynlp.md\r\n\r\n**Extended description:**\r\nAdded the introductory guide to HYNLP (Hybrid Natural Language Processing) in the \"Intro_to_hynlp.md\" file. 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