Ranking Answers by Hierarchical Topic Models

1. Topic models are hierarchical probabilistic models for the statistical analysis of document collections. It assumes that each document comprises a mixture of latent topics and each topic can be represented by a distribution over vocabulary. Dimensionality for a large corpus of unstructured documents can be reduced by modeling with these exchangeable topics. In previous work, we designed a multi-pipe structure for question answering (QA) systems by nesting keyword search, classical Natural Language Processing (NLP) techniques and prototype detections. In this research, we use those technologies to select a set of sentences as candidate answers. We then use topic models to ... (Read Full Article)

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