Prediction games and arcing algorithms
Neural Computation
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic Summarization of Japanese Sentences and its Application to a WWW KWIC Index
SAINT '01 Proceedings of the 2001 Symposium on Applications and the Internet (SAINT 2001)
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Japanese morphological analyzer using word co-occurrence: JTAG
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Extracting causal knowledge from a medical database using graphical patterns
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
An analysis of a high-performance japanese question answering system
ACM Transactions on Asian Language Information Processing (TALIP)
Automatic detection of causal relations for Question Answering
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Automatic question answering using the web: Beyond the Factoid
Information Retrieval
Exploiting syntactic patterns as clues in zero-anaphora resolution
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Soft pattern matching models for definitional question answering
ACM Transactions on Information Systems (TOIS)
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Evaluating discourse-based answer extraction for why-question answering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Paragraph retrieval for why-question answering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Investigating the characteristics of causal relations in Japanese text
CorpusAnno '05 Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky
Developing an approach for why-question answering
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Semantic role labeling as sequential tagging
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Causal relation extraction using cue phrase and lexical pair probabilities
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Why text segment classification based on part of speech feature selection
DS'10 Proceedings of the 13th international conference on Discovery science
Node-first causal network extraction for trend analysis based on web mining
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Towards domain independent why text segment classification based on bag of function words
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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This article describes our approach for answering why-questions that we initially introduced at NTCIR-6 QAC-4. The approach automatically acquires causal expression patterns from relation-annotated corpora by abstracting text spans annotated with a causal relation and by mining syntactic patterns that are useful for distinguishing sentences annotated with a causal relation from those annotated with other relations. We use these automatically acquired causal expression patterns to create features to represent answer candidates, and use these features together with other possible features related to causality to train an answer candidate ranker that maximizes the QA performance with regards to the corpus of why-questions and answers. NAZEQA, a Japanese why-QA system based on our approach, clearly outperforms baselines with a Mean Reciprocal Rank (top-5) of 0.223 when sentences are used as answers and with a MRR (top-5) of 0.326 when paragraphs are used as answers, making it presumably the best-performing fully implemented why-QA system. Experimental results also verified the usefulness of the automatically acquired causal expression patterns.