The Use of Metadata, Web-derived Answer Patterns and Passage Context to Improve Reading Comprehension Performance

A reading comprehension (RC) system attempts to understand a document and returns an answer sentence when posed with a question. RC resembles the ad hoc question answering (QA) task that aims to extract an answer from a collection of documents when posed with a question. However, since RC focuses only on a single document, the system needs to draw upon external knowledge sources to achieve deep analysis of passage sentences for answer sentence extraction. This paper proposes an approach towards RC that attempts to utilize external knowledge to improve performance beyond the baseline set by the bag-of-words (BOW) approach. Our approach emphasizes matching of metadata (i.e. verbs, named entities and base noun phrases) in passage context utilization and answer sentence extraction. We have also devised an automatic acquisition process for Web-derived answer patterns (AP) which utilizes question-answer pairs from TREC QA, the Google search engine and the Web. This approach gave improved RC performances for both the Remedia and ChungHwa corpora, attaining HumSent accuracies of 42% and 69% respectively. In particular, performance analysis based on Remedia shows that relative performances of 20.7% is due to metadata matching and a further 10.9% is due to the application of Web-derived answer patterns.