Metadata-Version: 2.1
Name: pyims
Version: 0.1.2
Summary: A python wrapper for the IMS Word Sense Disambiguation tool (Zhong and Ng, 2010)
Home-page: http://github.com/vishnumenon/pyims
Author: Vishnu Menon
Author-email: me@vishnumenon.com
License: MIT
Description: # PyIMS
        
        A 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+
        
        ## Installation
        
        First, 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:
        
        ```
        pip install pyims
        ```
        
        ## Usage
        
        ```python
        from pyims import PyIMS
        
        wsd = PyIMS("path/to/ims", "modelsDirName")
        print(wsd.disambiguate("I am interested in the interest rates at the bank.", probs=True, synsets=False))
        
        # If probs=True, returns a list of (token, probability_distribution) tuples where probability_distribution is a map of lemma to its probability
        # 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
        # If synsets=True, lemmas are replaced with the Synsets to which they belong
        # If synsets=False (default False), to access a lemma's synset, call lemma.synset()
        ```
        
        ## References
        
        * 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
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
