Sciweavers

Share
ACL
2007

Unsupervised Language Model Adaptation Incorporating Named Entity Information

9 years 1 months ago
Unsupervised Language Model Adaptation Incorporating Named Entity Information
Language model (LM) adaptation is important for both speech and language processing. It is often achieved by combining a generic LM with a topic-specific model that is more relevant to the target document. Unlike previous work on unsupervised LM adaptation, this paper investigates how effectively using named entity (NE) information, instead of considering all the words, helps LM adaptation. We evaluate two latent topic analysis approaches in this paper, namely, clustering and Latent Dirichlet Allocation (LDA). In addition, a new dynamically adapted weighting scheme for topic mixture models is proposed based on LDA topic analysis. Our experimental results show that the NE-driven LM adaptation framework outperforms the baseline generic LM. The best result is obtained using the LDA-based approach by expanding the named entities with syntactically filtered words, together with using a large number of topics, which yields a perplexity reduction of 14.23% compared to the baseline generic LM...
Feifan Liu, Yang Liu
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ACL
Authors Feifan Liu, Yang Liu
Comments (0)
books