Improving Passage Retrieval in Question Answering Using NLP

This paper describes an approach for the integration of linguistic information in passage retrieval in an open-source question answering system for Dutch. Annotation produced by the wide-coverage dependency parser Alpino is stored in multiple index layers to be matched with natural language question that have been analyzed by the same parser. We present a genetic algorithm to select features to be included in retrieval queries and for optimizing keyword weights. The system is trained on questions annotated with their answers from the competition on Dutch question answering within the Cross-Language Evaluation Forum (CLEF). The optimization yielded a significant improvement of about 19% in mean reciprocal rank scores on unseen evaluation data compared to the base-line using traditional information retrieval with plain text keywords.