Nonlinear Interpolation of Topic Models for Language Model Adaptation

Topic adaptation for language modeling is concerned with adjusting the probabilities in a language model to better reflect the expected frequencies of topical words for a new document. The language model to be adapted is usually built from large amounts of training text and is considered representative of the current domain. In order to adapt this model for a new document, the topic (or topics) of the new document are identified. Then, the probabilities of words that are more likely to occur in the identified topic(s) than in general are boosted, and the probabilities of words that are unlikely for the identified topic(s) are suppressed. We present