Paper: Evaluating Unsupervised Ensembles when applied to Word Sense Induction

ACL ID W12-3305
Title Evaluating Unsupervised Ensembles when applied to Word Sense Induction
Venue Annual Meeting of the Association of Computational Linguistics
Session Student Session
Year 2012
Authors

Ensembles combine knowledge from distinct machine learning approaches into a general flexible system. While supervised ensembles frequently show great benefit, unsupervised ensembles prove to be more challenging. We propose evaluating various unsupervised en- sembles when applied to the unsupervised task of Word Sense Induction with a framework for combining diverse feature spaces and cluster- ing algorithms. We evaluate our system us- ing standard shared tasks and also introduce new automated semantic evaluations and su- pervised baselines, both of which highlight the current limitations of existing Word Sense In- duction evaluations.