Bridging the lexical gap between the user's question and the question-answer pairs in the Q&A archives has been a major challenge for Q&A retrieval. State-of-the-art approaches address this issue by implicitly expanding the queries with additional words using statistical translation models. While useful, the effectiveness of these models is highly dependant on the availability of quality corpus in the absence of which they are troubled by noise issues. Moreover these models perform word based expansion in a context agnostic manner resulting in translation that might be mixed and fairly general. This results in degraded retrieval performance. In this work we address the above issues by extending the lexical word based translation model to incorporate semantic concepts (entities). We explore strategies to learn the translation probabilities between words and the concepts using the Q&A archives and a popular entity catalog. Experiments conducted on a large scale real data show that the proposed techniques are promising.
Singh et al. REF was extended the word-based translation model by incorporating semantic information and explored strategies to learn the translation probabilities between words and concepts using the cQA archives and an entity catalog.
Entity based Q&A Retrieval
EMNLP
Computer Science