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Period end? , etc.On the other hand, many online question-answering services, such as automaticanswering services, call centers, helpdesk and Q&A sites, have already beenestablished on the Internet and telephone networks. Q&A sites edit various questionsand answers in FAQ style presentation. The queries are not restricted tocertain domains, and the answers are written by the general public. Contrary tothe queries in TREC and NTCIR, their queries comprise many multiple sentencequeries.There are two major differences between queries in TREC and in actual QAServices. The first difference is the length of the queries and the number of thesentences, and the second difference is the question type. A TREC query isbasically a single sentence query (SSQ). Whereas a query in a QA service is oftena multiple sentence query (MSQ). The questions of TREC require mainly factoidanswers, such as a Person, an Organization and Location, but QA services musthandle many non-factoid questions requiring descriptive answers that consist ofa sentence or more, such as a Definition, a How-to and an Opinion, in additionto factoid questions. Although QA services have been supported by non-factoidMSQ, answers are extracted by humans. Figure 1.1 shows division of questionansweringsegmented by query, question and answer types in a tabular form.The queries in TREC can be assigned in the lower part of this table for factiodquestions, and that in actual Q&A services includes divisions in the upper partof this table. In the upperhalf of this table, this thesis mainly deals with queriesconsisting of multiple sentences and non-factoid questions requiring answers by asentence or more. In this thesis, this type of answer is called descriptive answer.To advance technologies of open domain question-answering to one that can applymore expanded query types such as those appearing in actual QA services, we haveto develop open domain question-answering that can deal with multi-sentencequeries and that handle questions requiring descriptive answers.1.2 Focus of this researchIn this thesis, we focus on two essential components to realize open domain multisentencequestion-answering. The first focus is Question Segment Extraction and2
The Number of Question per QuerySingle QuestionMultiple QuestionsSingle Questionsentencenon-longerQuestion FocusfactoidfactoidwordphrasesentencelongerAnswer UnitwordphraseSingle Sentence QueryMultiple Sentence QueryThe Number of Sentence per QueryFigure 1.1. Division of Queries and Answer Types.Question Type Identification for multi-sentence queries. The second is extractionof descriptive answers from Web pages toward question-answering requiring descriptiveanswers. We describe these two focuses in detail below.Question Segment Extraction and Question Type IdentificationIn what way is a multi-sentence query different from a single sentence query? If amulti-sentence query is merely a set of SSQs, known question type identificationmethods would be directly applicable. A multi-sentence query can contain multiplequestions. To avoid misunderstanding, each question included in a query iscalled a question item in this thesis as appropriate. Using this term, we firstlyhave to know how a query is decomposed into question items, and what contextof a question item should be considered to classify the question item into eitherof the question types. Here, we call a set of sentences necessarily required toidentify a question item and its type question segment. We also call the processby which a multi-sentence query is separated into question segments and thenidentify their question type of segments as question type identification. In thisthesis, we clarify the structure of question segments and the conditions of ques-3
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Table 4.1. The Definitions of Descr
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4.3.3 Overview of datasetsTable 4.2
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Table 4.4. Categorization of n Obje
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4.3.6 DiscussionIn the field of nat
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4.4 Description type based answer c
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R = |Rc||Ra|(4.10)Varying combinati
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Chapter 5Extraction of ProceduralEx
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Table 5.2. Domain and Type of List.
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Figure 5.2. Collection of Lists fro
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Table 5.3. Types of Tags.Tag types
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domain with the document set in the
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Table 5.6. Result of Close-Domain.C
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Table 5.9. Comparison of SVM and De
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Sentence : “ [menyu] w o s ent a
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• It is applicable in case that m
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• For extraction of procedural ex
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[10] Regina Barzilay. Information F
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[29] Yoav Freund and Robert E. Scha
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[47] Chiori Hori, Takaaki Hori, Hid
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[63] Mingzhe Jin. Authorship attrib
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[84] Christopher D. Manning and Hin
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of International World Wide Web Con
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[122] Akihiro Shinmori, Manabu Okum
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[141] Akihiro Tamura, Hiroya Takamu
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[161] Yudong Yang and HongJiang Zha
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[187] . n-gram . , Vol. 23,No. 5,
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- (Evaluation) Yes-No Yes-No (Ho
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(Analysis) : (Fact) : (Instance)
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