Paraphrasing using given and new information in a question-answer system
ACL '79 Proceedings of the 17th annual meeting on Association for Computational Linguistics
Deep Read: a reading comprehension system
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Lexical query paraphrasing for document retrieval
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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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HLT '02 Proceedings of the second international conference on Human Language Technology Research
Automatic paraphrase acquisition from news articles
HLT '02 Proceedings of the second international conference on Human Language Technology Research
A perspective-based approach for solving textual entailment recognition
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
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This paper describes the work on reading comprehension system, which accepts arbitrary articles as input and then generates answers according to the questions about the article. A new method to implement reading comprehension system is proposed in this paper. There are three steps in this system. First, the article will be parsed on the paragraph, sentence and phrase level. Second, the information is extracted from all sentences, and then appended to the knowledge model. Finally, the questions are answered by using knowledge model. With the experimental corpus the accuracy rate of knowledge matching is 62.5%, and accuracy rate of question answer is 64.8% with the system knowledge model.