The nature of statistical learning theory
The nature of statistical learning theory
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Evaluation of resources for question answering evaluation
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Extraction of Chinese compound words: an experimental study on a very large corpus
CLPW '00 Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 12
A question/answer typology with surface text patterns
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Integrated Computer-Aided Engineering
Detecting data records in semi-structured web sites based on text token clustering
Integrated Computer-Aided Engineering
A knowledge retrieval model using ontology mining and user profiling
Integrated Computer-Aided Engineering
Utilizing phrase-similarity measures for detecting and clustering informative RSS news articles
Integrated Computer-Aided Engineering
Ontology-based inference for causal explanation
Integrated Computer-Aided Engineering
Rule-based dependency models for security protocol analysis
Integrated Computer-Aided Engineering
Boosting Chinese Question Answering with Two Lightweight Methods: ABSPs and SCO-QAT
ACM Transactions on Asian Language Information Processing (TALIP)
Semantic pattern learning through maximum entropy-based WSD technique
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
New information distance measure and its application in question answering system
Journal of Computer Science and Technology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Integrated Computer-Aided Engineering
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We propose an alignment-based surface pattern approach, called ABSP, which integrates semantic information into syntactic patterns for question answering (QA). ABSP employs a new strategy to extract surface patterns from non-segmented passages. It uses the surface patterns to extract important terms from questions, and then constructs the terms' relations from sentences in the corpus. Finally, the relations are used to rank answer candidates. Our experiments show that ABSP is highly accurate, and it can be incorporated into other QA systems that have high coverage. It can also be used in cross-lingual QA systems. The approach is robust and portable to other domains.