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Table 3.4. Examples of Relation between Questions in a Sentence.TypeQuestion Segments1 Are there any non-destructive dating techniques forfossils? If exists, how precisely does it datethem?2 What era did he live in and what did he do in thattime?3 I would like to go a festival near here. Do anyone knowwhen and where any exciting festivalshold?4 Since that time, people may used the word. What year didit begin and what trigger did happen in thetime?5 Please send me any information about exercise books andlearning methods28
this thesis. This Chapter proposes a simple model for identifying the questionsegment and question type as a study aid in identifying the type of multi-sentencequeries.3.4 Chunking-based question segmentation andtype identificationThe question type annotation conducted in Section 3.3 was a task in which humansextracted question segments and identified question types at the same time.However, these two processes do not necessarily have to be performed at the sametime. For type identification of multi-sentence queries, Tamura et.al.[141] haveproposed a two-step method by which the question segment and question typeare separately identified. Their study is the only previous study concerning thematters discussed in their paper as far as we know. With the method by Tamuraet.al., the question segment is limited to one sentence and no identification ofquestion type for the cases containing more than one question has not been proposed.Their method is a learning-based method using SVMs(Support VectorMachines [153]) and the features effective in question segment extraction andquestion type identification have been analyzed in detail. Therefore, their reportis worth consideration in this thesis. According to their report, the experimentalresults obtained when segment extraction (called core sentence extraction) andquestion type identification are separately performed are better than those obtainedwhen they are performed at the same time. If this is true, there should bea condition that is effective in question segment extraction but disadvantageousin question type identification, or vice versa. In the Tamura’s tests, better resultswere obtained by using not only the features of the sentence to be extracted butalso the preceding and following contexts. In addition, it was reported that thelonger the context to be used the better the accuracy became. If this is correct,better results may be obtained by extracting a question segment considering notonly the local context around the question segment subject to extraction andidentification but also the context of the entire question article.However, the study by Tamura et.al. remains problematic. With the SVMbasedmethod proposed in their paper, it is predictable that there will be an29
Page 1: NAIST-IS-DD0061208Doctoral Disserta
Page 4 and 5: This thesis studies two fundamental
Page 6 and 7: F 0.8 F 0.7 , , , , , iv
Page 8 and 9: 3.4.4 Experimental settings . . . .
Page 10 and 11: List of Tables3.1 Definitions of Qu
Page 12 and 13: List of Figures1.1 Division of Quer
Page 15 and 16: Chapter 1Introduction1.1 Motivation
Page 17 and 18: The Number of Question per QuerySin
Page 19: answers. Although different distrib
Page 22 and 23: ComputerHumanComputerHumanSpecializ
Page 24 and 25: Blog PageMultipleSentenceQueryQ1Q2e
Page 26 and 27: Data FlowQuestion TypeIdentificatio
Page 28 and 29: I will introduce fundamental techno
Page 31 and 32: Chapter 3Question Type Identificati
Page 33 and 34: used for question sentence type ide
Page 35 and 36: (1) We pay car commuter employees a
Page 37 and 38: Table 3.2. Classified Given Questio
Page 39 and 40: Figure 3.2. Combinations of Questio
Page 41: Plants can grow indoors. In additio
Page 45 and 46: as Inside/Outside [113, 116] and St
Page 47 and 48: Step 1 Segment a question article i
Page 49 and 50: elements be Θ. Then the degree of
Page 51 and 52: Table 3.5. Transition of Question T
Page 53 and 54: Table 3.6. Summary of Experimental
Page 55 and 56: Table 3.8. Results of Chunking Vary
Page 57 and 58: However when using IOB2/IOE2/IOBES,
Page 59 and 60: 3.6 Related workIdentification of t
Page 61 and 62: Chapter 4Categorization of Descript
Page 63 and 64: 30, 77, 154] and those asking reput
Page 65 and 66: elements were usually eliminated. H
Page 67 and 68: Table 4.1. The Definitions of Descr
Page 69 and 70: 4.3.3 Overview of datasetsTable 4.2
Page 71 and 72: Table 4.4. Categorization of n Obje
Page 73 and 74: 4.3.6 DiscussionIn the field of nat
Page 75 and 76: 4.4 Description type based answer c
Page 77 and 78: R = |Rc||Ra|(4.10)Varying combinati
Page 79 and 80: Chapter 5Extraction of ProceduralEx
Page 81 and 82: Table 5.2. Domain and Type of List.
Page 83 and 84: Figure 5.2. Collection of Lists fro
Page 85 and 86: Table 5.3. Types of Tags.Tag types
Page 87 and 88: domain with the document set in the
Page 89 and 90: Table 5.6. Result of Close-Domain.C
Page 91 and 92: 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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List of PublicationJournal Papers[1
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