file - ChaSen - 奈良先端科学技術大学院大学

More documents

Recommendations

Info

considerable difference in computational cost between the cases when questionsegment extraction and question type identification are performed at the sametime and when not. In the method expanded to the case of query containingmore than one question item, the sign and score that the SVM gives to the coresentence are used as a criteria for selecting a core sentence containing questionitems [185]. However, no evidence has been presented to prove that core sentencescan be properly extracted according to such a criteria. Moreover, an assumptionthat a question segment consists of one sentence is a more fundamental problemthat is different from observations of real queries.In an attempt to solve these problems, we propose a method by which questionsegment extraction and its question type identification are performed at thesame time to solve the sentence-chunking problem using Conditional RandomFields (CRF, a machine learning method). This method is capable of executingquestion segment extraction and question type identification at the same timeand is also advantageous in terms of computational cost. This can also apply, ina natural way, to query articles containing more than one question segment. Ascompared with SVM, CRF has the property of selecting the optimum model forthe entire space of solutions and therefore, it should be advantageous in the tasksdiscussed in this thesis. It has been reported that CRF has higher performance inseveral tasks than SVM and therefore, CRF is comparable to SVM, as a learningalgorithm. This section describes question type identification based on sentencechunking using CRF.3.4.1 ChunkingChunking is a process of identifying chunks that indicates some sort of visual orsemantic unit. Chunks as used in the field of natural language processing oftenindicate the noun phrase and paragraph, or lexical and grammatical units. Inthis case, chunking is a processing which forms morphemes and sentences intochunks such as noun phrases and paragraphs.Although there are various expression of expressing chunks, we adopted themethod by which a tag indicating the status of a chunk is given to each sentence,which permits modeling in the same framework as for the conventional problemof tagging to morphemes and noun phrases. For this task, previous methods such30
as Inside/Outside [113, 116] and Start/End [151] have been proposed. Kudo et.al.[73] summarized them into five expressions of IOB1, IOB2, IOE1, IOE2, andIOBES(Start/End). First, the following ten kinds of chunk statuses are defined.I1 The word in the present position is part of the chunk.I2 The word in the present position is a middle word other than at the start orend of the chunk consisting of three words or more.B1 The word in the present position is the start of the chunk immediately followinga chunk.B2 A tag is given to the start of every chunk.B3 The word in the present position is the word at the start of the chunk consistingof two words or more.E1 A tag is given to the word at the end of the chunk immediately preceding achunk.E2 A tag is given to the word at the end of every chunk.E3 The word in the present position is the word at the end of the chunk consistingof two words or more.S The word in the present position singularly consisting of one chunk.O The word in the present position is not included in the chunk.At this time, IOB1, IOB2, IOE1, IOE2, and IOBES are models that performtagging to meet the combinations of the following rules based on the above rules.IOB1 I1, O, B1IOB2 I1, O, B2IOE1 I1, O, E1IOE2 I1, O, E2IOBES I2, O, B3, E3, S31
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 and 42: Plants can grow indoors. In additio
Page 43: this thesis. This Chapter proposes
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
Page 93: Sentence : “ [menyu] w o s ent a
Page 96 and 97:
• It is applicable in case that m
Page 98 and 99:
• For extraction of procedural ex
Page 100 and 101:
[10] Regina Barzilay. Information F
Page 102 and 103:
[29] Yoav Freund and Robert E. Scha
Page 104 and 105:
[47] Chiori Hori, Takaaki Hori, Hid
Page 106 and 107:
[63] Mingzhe Jin. Authorship attrib
Page 108 and 109:
[84] Christopher D. Manning and Hin
Page 110 and 111:
of International World Wide Web Con
Page 112 and 113:
[122] Akihiro Shinmori, Manabu Okum
Page 114 and 115:
[141] Akihiro Tamura, Hiroya Takamu
Page 116 and 117:
[161] Yudong Yang and HongJiang Zha
Page 118 and 119:
[187] . n-gram . , Vol. 23,No. 5,
Page 120 and 121:
- (Evaluation) Yes-No Yes-No (Ho
Page 122 and 123:
(Analysis) : (Fact) : (Instance)
Page 125:
List of PublicationJournal Papers[1
show all

file - ChaSen - 奈良先端科学技術大学院大学

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?