Paper: Unsupervised Language Model Adaptation Incorporating Named Entity Information

ACL ID P07-1085
Title Unsupervised Language Model Adaptation Incorporating Named Entity Information
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

Language model (LM) adaptation is im- portant for both speech and language processing. It is often achieved by com- bining a generic LM with a topic-specific model that is more relevant to the target document. Unlike previous work on un- supervised LM adaptation, this paper in- vestigates how effectively using named entity (NE) information, instead of con- sidering all the words, helps LM adapta- tion. We evaluate two latent topic analysis approaches in this paper, namely, cluster- ing and Latent Dirichlet Allocation (LDA). In addition, a new dynamically adapted weighting scheme for topic mix- ture models is proposed based on LDA topic analysis. Our experimental results show that the NE-driven LM adaptation framework outperforms the baseline ge- neric LM. The best result is obtained us- in...