file - ChaSen - 奈良先端科学技術大学院大学

This thesis studies two fundamental components for realizing this kind ofquestion-answering, that are, domain-independent multi-sentence question analysisand descriptive answer extraction from large amount of documents. In questionanalysis, this thesis examines methodologies to decompose a multi-sentencequery to question items and identify their question types. To achieve these tasks,this thesis proposes a new efficient sentence-chunking based technique. In additionto conventional single sentence questions, the proposed method can handlea multi-sentence query comprising multiple question items, as well as a questionitem comprising multiple sentences in the same framework.Additionally, this thesis studies the typology of descriptive answers in Q&Aservices and the automatic categorization of descriptive answers in terms of thetypology. Exploiting features of functional words that have not been counted inthe previous work of text categorization, this study proposes new techniques ofanswer categorization based on the description type. The experimental resultsshowed a high accuracy of the proposed method, 0.8 F-measure, for the threedescription types: definition, chronological order and procedure.Moreover, this thesis pays attention to the answer extraction from web pages.Our approach accurately extracts answer candidates by identifying the descriptiontype. The new method is based on sequential pattern mining and machinelearning techniques to extract lists of procedural expressions. As a result of experiments,this method showed a high performance, more than 0.7 F-measure,when extracting lists of procedural expressions.Keywords:question-answering, multiple sentence question, non-factoid question, descriptiveanswer, Web question-answering, procedural expressionsii