Paper: Probabilistic Domain Modelling With Contextualized Distributional Semantic Vectors

ACL ID P13-1039
Title Probabilistic Domain Modelling With Contextualized Distributional Semantic Vectors
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Generative probabilistic models have been used for content modelling and template induction, and are typically trained on small corpora in the target domain. In contrast, vector space models of distribu- tional semantics are trained on large cor- pora, but are typically applied to domain- general lexical disambiguation tasks. We introduce Distributional Semantic Hidden Markov Models, a novel variant of a hid- den Markov model that integrates these two approaches by incorporating contex- tualized distributional semantic vectors into a generative model as observed emis- sions. Experiments in slot induction show that our approach yields improvements in learning coherent entity clusters in a do- main. In a subsequent extrinsic evalua- tion, we show that these improvements are also reflected in...