A Web Knowledge Based Approach for Complex Question Answering

Current researches on Question Answering concern more complex questions than factoid ones. Since complex questions are investigated by many researches, how to acquire accurate answers still becomes a core problem for complex QA. In this paper, we propose an approach that estimates the similarity by topic model. After summarizing relevant texts from web knowledge bases, an answer sentence acquisition model based on Probabilistic Latent Semantic Analysis is introduced to seek sentences, in which the topic is similar to those in definition set. Then, an answer ranking model is employed to select both statistically and semantically similar sentences between sentences retrieved and sentences in the relevant text set. Finally, sentences are ranked as answer candidates according to their scores. Experiments show that our approach achieves an increase of 5.19% F-score than the baseline system.