Unsupervised Language Model Adaptation Incorporating Named Entity Information
Feifan Liu, Yang Liu · 2007
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-ing the LDA-based approach by expanding the named entities with syntac-tically filtered words, together with using a large number of topics, which yields a perplexity reduction of 14.23 % compared to the baseline generic LM. 1