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        "author": "Vishnu Menon",
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        "description": "# PyIMS\n\nA simple python wrapper around the IMS (It Makes Sense) word-sense disambiguation toolkit, that integrates it with NLTK's WordNet interface. Requires IMS to be downloaded separately, which requires a working Java installation. Also requires NLTK with WordNet downloaded. Only tested in Linux, might not work in other OSes. Built for python 3.6+\n\n## Installation\n\nFirst, follow the instructions in [Section 3 of the IMS Readme](https://raw.githubusercontent.com/NUNLP/ims/master/README.txt) to install the components of IMS into some directory; extract the models from [here](http://www.comp.nus.edu.sg/~nlp/corpora.html#onemilwsd) (step 3.e) into the same directory as in steps 3.b and 3.c. Then, run:\n\n```\npip install pyims\n```\n\n## Usage\n\n```python\nfrom pyims import PyIMS\n\nwsd = PyIMS(\"path/to/ims\", \"modelsDirName\")\nprint(wsd.disambiguate(\"I am interested in the interest rates at the bank.\", probs=True, synsets=False))\n\n# If probs=True, returns a list of (token, probability_distribution) tuples where probability_distribution is a map of lemma to its probability\n# If probs=False (default False), returns a list of (token, lemma) where lemma is the most probable word-sense in WordNet for the given token\n# If synsets=True, lemmas are replaced with the Synsets to which they belong\n# If synsets=False (default False), to access a lemma's synset, call lemma.synset()\n```\n\n## References\n\n* Zhong, Zhi and Ng, Hwee Tou. 2010. It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text. In Proceedings of the ACL 2010 System Demonstrations, pages 78--83, Uppsala, Sweden\n\n\n",
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