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        "author": "Damir Cavar, Oren Baldinger, Maanvitha Gongalla, Anurag Kumar, Murali Kammili, Boli Fang",
        "author_email": "damir@cavar.me",
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        "description": "# Xrenner to JSON-NLP\n\n(C) 2019 by [Damir Cavar], [Oren Baldinger], Maanvitha Gongalla, Anurag Kumar, Murali Kammili, Boli Fang\n\nBrought to you by the [NLP-Lab.org]!\n\n\n## Introduction\n\n[Xrenner] wrapper for [JSON-NLP]. [Xrenner] specializes in coreference and anaphora resolution, in a more highly annotated manner \nthan just a coreference chain.\n\n## Required Dependency Parse\n\nXrenner requires a [Dependency Parse](https://en.wikipedia.org/wiki/Dependency_grammar) in [CoNLL-U] format. \nThis can come from [CoreNLP], or another parser that provides universal dependencies in [CoNNL-U] format.\nThere are two ways to accomplish this:\n\n### CoreNLP Server\n\nThe `XrennerPipeline` class will take care of the details, however it requires an available [CoreNLP] server.\nThe easiest way to create one is with [Docker]:\n\n    docker pull nlpbox/corenlp\n    docker run -p 9000:9000 -ti nlpbox/corenlp\n\nTo test this, open a new tab,\n\n    wget -q --post-data \"Although they didn't like it, they accepted the offer.\"   'localhost:9000/?properties={\"annotators\":\"depparse\",\"outputFormat\":\"conll\"}' -O /dev/stdout\n\nYou then need to create a `.env` file in the root of the project, follow the example in `sample_env`.\nThe default entry that corresponds to the [Docker] command above is: \n\n    CORENLP_SERVER=http://localhost:9000\n\n### Provide your own CoNLL-U\n\nUse the `XrennerPipeline.process_conll` function, with your conll data passed as a string via\nthe `conll` argument.\n\nYou may find the `pyjsonnlp.conversion.to_conllu` function helpful for converting [JSON-NLP],\nmaybe from [spaCy], to [CoNLL-U].\n\n\n## Microservice\n\nThe [JSON-NLP] repository provides a Microservice class, with a pre-built implementation of [Flask]. To run it, execute:\n\n    python xrennerjsonnlp/server.py\n\nSince `server.py` extends the [Flask] app, a WSGI file would contain:\n\n    from xrennerjsonnlp.server import app as application\n\nText is provided to the microservice with the `text` parameter, via either `GET` or `POST`. If you pass `url` as a parameter, the microservice will scrape that url and process the text of the website.\n\nHere is an example `GET` call:\n\n    http://localhost:5000?text=John went to the store. He bought some milk.\n\nThe `process_conll` endpoint mentioned above is available at the `/process_conll`\nURI. Instead of passing `text`, pass `conll`. A POST operation will be easier than GET \nin this situation.\n\n\n\n[Damir Cavar]: http://damir.cavar.me/ \"Damir Cavar\"\n[Oren Baldinger]: https://oren.baldinger.me/ \"Oren Baldinger\"\n[NLP-Lab.org]: http://nlp-lab.org/ \"NLP-Lab.org\"\n[JSON-NLP]: https://github.com/dcavar/JSON-NLP \"JSON-NLP\"\n[Flair]: https://github.com/zalandoresearch/flair \"Flair\"\n[spaCy]: https://spacy.io/ \"spaCy\"\n[NLTK]: http://nltk.org/ \"Natural Language Processing Toolkit\"\n[Polyglot]: https://github.com/aboSamoor/polyglot \"Polyglot\"\n[Xrenner]: https://github.com/amir-zeldes/xrenner \"Xrenner\"\n[CONLL-U]: https://universaldependencies.org/format.html \"CONLL-U\"\n[Docker]: https://www.docker.com/ \"Docker\"\n[CoreNLP]: https://stanfordnlp.github.io/CoreNLP/ \"Stanford CoreNLP\"\n[Flask]: http://flask.pocoo.org/ \"Flask\"\n\n\n",
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