Learning predominant sense of words with HDP topic model
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hdp_output
lemmatiser_tools
predom_data
ComputeSenseRanking.py
GenMacmillanSenses.py
GenWordnetSenses.py
README.md
run_predom_sense.sh

README.md

This package contains scripts and python tools for learning predominant sense using HDP, a hierarchical topic model.

Directory Structure and Files

  • ComputeSenseRanking.py: program that computes sense ranking and predominant sense.
  • GenMacmillanSenses.py: program that generates Macmillan word distribution of senses.
  • GenWordnetSenses.py: program that generates WordNet word distribution of senses.
  • hdp_output: contains an example of the output generated by HDP.
  • lemmatiser_tools: contains OpenNLP and Morpha for tokenising and lemmatising the words when generating dictionary word distribution.
  • predom_data: contains example input files for running the program.
  • run_predom_sense.sh: script that drives the execution of the program.

Running the System

Prerequisites: -Wordnet -HDP topic model (https://github.com/jhlau/hdp-wsi) -Python lxml

  1. Create lemmas.txt: a text file that contains the target lemmas of interest.
  2. Run HDP to induce senses for the target lemmas.
  3. If using Macmillan dictionary, create a directory that contains xml files of word definitions. If using WordNet, make sure it is installed (i.e. "wn word -over" command needs to work).
  4. Set up the parameters in run_predom_sense.sh and execute the script!

Input Format

  • lemmas.txt: one line per lemma, in the format word.n (for nouns) or word.v (for verbs). E.g. "bank.n"

Licensing

Publications

  • Jey Han Lau, Paul Cook, Diana McCarthy, Spandana Gella and Timothy Baldwin (to appear). Learning Word Sense Distributions, Detecting Unattested Senses and Identifying Novel Senses Using Topic Models. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL 2014), Baltimore, USA.
  • Jey Han Lau, Paul Cook, Diana McCarthy, David Newman and Timothy Baldwin (2012). Word Sense Induction for Novel Sense Detection. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), Avignon, France, pp 591—601.