Relation Extraction from Community Generated Question-Answer Pairs

Denis Savenkov, Wei-Lwun Lu, Jeff Dalton, Eugene Agichtein

Abstract: Community question answering (CQA) websites contain millions of question and answer (QnA) pairs that represent real users’ interests. Traditional methods for relation extraction from natural language text operate over individual sentences. However answer text is sometimes hard to understand without knowing the question, e.g., it may not name the subject or relation of the question. This work presents a novel model for relation extraction from CQA data, which uses discourse of QnA pairs to predict relations between entities mentioned in question and answer sentences. Experiments on 2 publicly available datasets demonstrate that the model can extract from 20% to 40% additional relation triples, not extracted by existing sentence-based models.