This package contains the scripts and various python tools for running HDP to induce word senses.

• run_wsi.sh: the main script for driving the WSI system.
• topicmodelling: contains various document preprocessing tools and the HDP program.
• wsi_input: contains the input for the WSI system.
• wsi_output: contains the output for the WSI system.

• Generate the input files for the system (format described below).
• Fix the GSL library path for compiling hdp (in topicmodelling/hdp/Makefile).
• Set up HDP parameters (such as the gamma and alpha paramters, stopwords); these settings are in the topic model script (topicmodelling/run_topicmodel.sh).
• Set up WSI parameters; these settings are in the WSI script (run_wsi.sh).
• Execute run_wsi.sh.

• num_test_instances.all.txt: contains the number of TEST instances/documents for the WSI system, one line per lemma.
• all/target_word.lemma: contains the instances/documents for the WSI system, one file per lemma and one line per instance/document.

Note that the total number of instances may be greater than the TEST instances - the test instances are instances of interest that will be evaluated. The remaining instances are additional/training instances for the topic model to produce higher quality senses. The test instances should ALWAYS be put at the beginning of the input file (all/target_word.lemma), i.e. if there are 100 test instances and 200 additional/training instances, the first 100 lines of the input file should contain the 100 test instances, and the next 200 lines are the additional/training instances.

• tm_wsi: contains the sense distribution for each instance/document (one document per line). The instances correpond to the original order of the input file (all/target_word.lemma).
• tm_wsi.topics: gives the top-10 terms for the induced topics (one topic per line).
• topic_worprob: contains python pickle files which contains the word distribution of the induced senses. for each lemma.

• MIT license - http://opensource.org/licenses/MIT.

• 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.

• Jey Han Lau, Paul Cook, and Timothy Baldwin (to appear). unimelb: Topic Modelling-based Word Sense Induction. In Proceedings of the 7th International Workshop on Semantic Evaluation (SemEval 2013).
• Jey Han Lau, Paul Cook, and Timothy Baldwin (to appear). unimelb: Topic Modelling-based Word Sense Induction for Web Snippet Clustering. In Proceedings of the 7th International Workshop on Semantic Evaluation (SemEval 2013).