LDA based similarity modeling for question answering


A. Celikyilmaz, D. Hakkani-Tiir, and G. Tur, “LDA based similarity modeling for question answering,” in Proc. Human Language Technologies: The 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2010, pp. 1–9.


We present an exploration of generative modeling for the question answering (QA) task to rank candidate passages. We investigate Latent Dirichlet Allocation (LDA) models to obtain ranking scores based on a novel similarity measure between a natural language question posed by the user and a candidate passage. We construct two models each one introducing deeper evaluations on latent characteristics of passages together with given question. With the new representation of topical structures on QA datasets, using a limited amount of world knowledge, we show improvements on performance of a QA ranking system.

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