Information extraction as a stepping stone toward story understanding
Understanding language understanding
Deep Read: a reading comprehension system
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Reading comprehension programs in a statistical-language-processing class
ANLP/NAACL-ReadingComp '00 Proceedings of the 2000 ANLP/NAACL Workshop on Reading comprehension tests as evaluation for computer-based language understanding sytems - Volume 6
A rule-based question answering system for reading comprehension tests
ANLP/NAACL-ReadingComp '00 Proceedings of the 2000 ANLP/NAACL Workshop on Reading comprehension tests as evaluation for computer-based language understanding sytems - Volume 6
A machine learning approach to answering questions for reading comprehension tests
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
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The reading comprehension (RC) task- accepting arbitrary text input (a story) and answering questions about it. The RC 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 semantic information to improve performance beyond the baseline set by the bag-of-words (BOW) approach. Our approach emphasizes matching of linguistic features (i.e. verbs, named entities and base noun phrases) and semantic extending for answer sentence extraction. The approach gave improved RC performance in the Remedia corpus, attaining HumSent accuracies of 41.3%. In particular, performance analysis shows that a relative performance of 19.7% is due to the application of linguistic feature matching and a further 10.3% is due to the semantic extending.