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        "author": "Sina",
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        "description": "# pynlp\n[![Build Status](https://travis-ci.org/sina-al/pynlp.svg?branch=master)](https://travis-ci.org/sina-al/pynlp)\n[![PyPI version](https://badge.fury.io/py/pynlp.svg)](https://badge.fury.io/py/pynlp)\n\nA *pythonic* wrapper for Stanford CoreNLP.\n\n<p align=\"center\">\n        <img src=\"https://media.giphy.com/media/l2QDNvOnIK6H2CRgY/giphy.gif\" >\n</p>\n\n## Description\nThis library provides a Python interface to [Stanford CoreNLP](https://stanfordnlp.github.io/CoreNLP/) built over [`corenlp_protobuf`](https://github.com/stanfordnlp/python-corenlp-protobuf). \n\n## Installation\n1. Download Stanford CoreNLP from the official [download page](https://stanfordnlp.github.io/CoreNLP/download.html).\n2. Unzip the file and set your `CORE_NLP` environment variable to point to the directory.\n3. Install `pynlp` from pip\n```\npip3 install pynlp\n```\n\n## Quick Start\n\n### Launch the server\nLauch the `StanfordCoreNLPServer` using the instruction given [here](https://stanfordnlp.github.io/CoreNLP/corenlp-server.html). *Alternatively*, simply run the module.\n```\npython3 -m pynlp\n```\n*By default, this lauches the server on localhost using port 9000 and 4gb ram for the JVM. Use the `--help` option for instruction on custom configurations.*\n\n### Example\n\nLet's start off with an excerpt from a CNN article.\n```python\ntext = ('GOP Sen. Rand Paul was assaulted in his home in Bowling Green, Kentucky, on Friday, '\n        'according to Kentucky State Police. State troopers responded to a call to the senator\\'s '\n        'residence at 3:21 p.m. Friday. Police arrested a man named Rene Albert Boucher, who they '\n        'allege \"intentionally assaulted\" Paul, causing him \"minor injury\". Boucher, 59, of Bowling '\n        'Green was charged with one count of fourth-degree assault. As of Saturday afternoon, he '\n        'was being held in the Warren County Regional Jail on a $5,000 bond.')\n```\n### Instantiate annotator\nHere we demonstrate the following annotators:\n* **Annotoators:** *tokenize, ssplit, pos, lemma, ner, entitymentions, coref, sentiment, quote, openie*\n* **Options:** *openie.resolve_coref*\n```python\nfrom pynlp import StanfordCoreNLP\n\nannotators = 'tokenize, ssplit, pos, lemma, ner, entitymentions, coref, sentiment, quote, openie'\noptions = {'openie.resolve_coref': True}\n\nnlp = StanfordCoreNLP(annotators=annotators, options=options)\n\n```\n### Annotate text\nThe `nlp` instance is callable. Use it to annotate the text and return a `Document` object.\n```python\ndocument = nlp(text)\n\nprint(document) # prints 'text'\n```\n#### Sentence splitting\nLet's test the *ssplit* annotator. A `Document` object iterates over its `Sentence` objects.\n```python\nfor index, sentence in enumerate(document):\n    print(index, sentence, sep=' )')\n```\nOutput:\n```\n0) GOP Sen. Rand Paul was assaulted in his home in Bowling Green, Kentucky, on Friday, according to Kentucky State Police.\n1) State troopers responded to a call to the senator's residence at 3:21 p.m. Friday.\n2) Police arrested a man named Rene Albert Boucher, who they allege \"intentionally assaulted\" Paul, causing him \"minor injury\".\n3) Boucher, 59, of Bowling Green was charged with one count of fourth-degree assault.\n4) As of Saturday afternoon, he was being held in the Warren County Regional Jail on a $5,000 bond.\n```\n#### Named entity recognition\nHow about finding all the people mentioned in the document?\n```python\n[str(entity) for entity in document.entities if entity.type == 'PERSON']\n```\nOutput:\n```\nOut[2]: ['Rand Paul', 'Rene Albert Boucher', 'Paul', 'Boucher']\n```\nWe may use named entities on a sentence level too.\n```python\nfirst_sentence = document[0]\nfor entity in first_sentence.entities:\n    print(entity, '({})'.format(entity.type))\n```\nOutput:\n```\nGOP (ORGANIZATION)\nRand Paul (PERSON)\nBowling Green (LOCATION)\nKentucky (LOCATION)\nFriday (DATE)\nKentucky State Police (ORGANIZATION)\n```\n#### Part-of-speech tagging\nLet's find all the 'VB' tags in the first sentence. A `Sentence` object iterates over `Token` objects.\n```python\nfor token in first_sentence:\n    if 'VB' in token.pos:\n        print(token, token.pos)\n```\nOutput:\n```\nwas VBD\nassaulted VBN\naccording VBG\n```\n#### Lemmatization\nUsing the same words, lets see the lemmas.\n```python\nfor token in first_sentence:\n    if 'VB' in token.pos:\n       print(token, '->', token.lemma)\n```\nOutput:\n```\nwas -> be\nassaulted -> assault\naccording -> accord\n```\n#### Coreference resultion\nLet's use pynlp to find the first `CorefChain` in the text.\n```python\nchain = document.coref_chains[0]\nprint(chain)\n```\nOutput:\n```\n((GOP Sen. Rand Paul))-[id=4] was assaulted in (his)-[id=5] home in Bowling Green, Kentucky, on Friday, according to Kentucky State Police.\nState troopers responded to a call to (the senator's)-[id=10] residence at 3:21 p.m. Friday.\nPolice arrested a man named Rene Albert Boucher, who they allege \"(intentionally assaulted\" Paul)-[id=16], causing him \"minor injury.\n```\nIn the string representation, coreferences are marked with parenthesis and the referent with double parenthesis.\nEach is also labelled with a `coref_id`. Let's have a closer look at the referent.\n```python\nref = chain.referent\nprint('Coreference: {}\\n'.format(ref))\n\nfor attr in 'type', 'number', 'animacy', 'gender':\n    print(attr,  getattr(ref, attr), sep=': ')\n\n# Note that we can also index coreferences by id\nassert chain[4].is_referent\n```\nOutput:\n```\nCoreference: Police\n\ntype: PROPER\nnumber: SINGULAR\nanimacy: ANIMATE\ngender: UNKNOWN\n```\n\n#### Quotes\nExtracting quotes from the text is simple.\n```python\nprint(document.quotes)\n```\nOutput:\n```\n[<Quote: \"intentionally assaulted\">, <Quote: \"minor injury\">]\n```\n\n#### TODO (annotation wrappers):\n- [x] ssplit\n- [ ] ner\n- [x] pos\n- [x] lemma\n- [x] coref\n- [x] quote\n- [ ] quote.attribution\n- [ ] parse\n- [ ] depparse\n- [x] entitymentions\n- [ ] openie\n- [ ] sentiment\n- [ ] relation\n- [ ] kbp\n- [ ] entitylink\n- [ ] 'options' examples i.e openie.resolve_coref\n\n### Saving annotations\n#### Write\nA pynlp document can be saved as a byte string.\n```python\nwith open('annotation.dat', 'wb') as file:\n    file.write(document.to_bytes())\n```\n#### Read\nTo load a pynlp document, instantiate a `Document` with the `from_bytes` class method.\n```python\nfrom pynlp import Document\n\nwith open('annotation.dat', 'rb') as file:\n    document = Document.from_bytes(file.read())\n```\n\n\n",
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