An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
On the MSE robustness of batching estimators
Proceedings of the 33nd conference on Winter simulation
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Evaluation of an extraction-based approach to answering definitional questions
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Automatically evaluating answers to definition questions
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Improvement of building field association term dictionary using passage retrieval
Information Processing and Management: an International Journal
Knowledge element extraction for knowledge-based learning resources organization
ICWL'07 Proceedings of the 6th international conference on Advances in web based learning
A social recommender mechanism for improving knowledge sharing in online forums
Information Processing and Management: an International Journal
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We propose answer extraction and ranking strategies for definitional question answering using linguistic features and definition terminology. A passage expansion technique based on simple anaphora resolution is introduced to retrieve more informative sentences, and a phrase extraction method based on syntactic information of the sentences is proposed to generate a more concise answer. In order to rank the phrases, we use several evidences including external definitions and definition terminology. Although external definitions are useful, it is obvious that they cannot cover all the possible targets. The definition terminology score which reflects how the phrase is definition-like is devised to assist the incomplete external definitions. Experimental results show that the proposed answer extraction and ranking method are effective and also show that our proposed system is comparable to state-of-the-art systems.