Paper: Measuring the Similarity between Automatically Generated Topics

ACL ID E14-4005
Title Measuring the Similarity between Automatically Generated Topics
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Previous approaches to the problem of measuring similarity between automati- cally generated topics have been based on comparison of the topics? word probability distributions. This paper presents alterna- tive approaches, including ones based on distributional semantics and knowledge- based measures, evaluated by compari- son with human judgements. The best performing methods provide reliable esti- mates of topic similarity comparable with human performance and should be used in preference to the word probability distri- bution measures used previously.