file - ChaSen - 奈良先端科学技術大学院大学

More documents

Recommendations

Info

Features of context for chunking a sentence s5 with B-W tag (window size = 3)Group A:m frequent POSfeature1 feature2 ... feature mGroup B:n POS atend of sentencefeature m+1 ... feature m+nchunk tagss1Commuting by...w1w2...wmw1,m+1...w1,m+nOs2To employees...w1w2...wmw2,m+1...w2,m+nB-Ds3My company ...nilnil...nilw3,m+1...w3,m+nOs4Managers ...nilnil...nilw4,m+1...w4,m+nOs5..w1nil...wmw5,m+1...w5,m+nB-Ws6Do you know...w1nil...wmw6,m+1...w6,m+nI-Ws7If you have ...w1w2...wmw7,m+1...w7,m+nOFigure 3.6. Example of data Format in Learning and Testing of Chunking.features in learning corpus, a thousand of frequent part of speeches are stored.Besides this experiment, the experiment exploiting only several words at beginningand end of sentence are performed. It is the reason why symbols and functionwords such as question mark and auxiliaries at the end of sentence are expectedto be effective for extraction of question segment, and interrogatives at beginningof sentence to work well for question type identification. The number of exploitedpart of speeches at the beginning and end of sentence varied one to five.The chunk tag sets comprising four types mentioned in Section 3.4.1 andIO tag set that does not distinct two adjacent question segments, are used forchunking. As the chunker implementing CRF, I used CRF++ supported byKudo. The learning parameters were set in default values.Features using this experiment only were combinations of part–of–speech(POS). Uni-gram and bi-gram of POS, and n words from beginning or1 http://chasen.org/˜taku/software/CRF++/38
Table 3.6. Summary of Experimental Settings.Features Set1 : uni-gram of POS all/content wordsSet2 : uni-gram + bi-gram of POSallSet3 : n POS at the end of santence n=1-5Set4 : n POS at sentence head and end n=1-5Chunk tags IO/IOB1/IOB2/IOE1/IOE2/IOBESWindow size one, three and five sentencesend of sentence were exploited. The number n was varied from 1 to 5. In the casethat only uni-gram was exploited, both a feature set only including content wordsand another set including all words were tested. Figure 3.6 represents the formatof feature set of learning and test data for CRF++. It is a matrix of features ofsentences. Each column is assigned to one feature and each cell in this matrixindicates a feature value corresponding to the sentence. In this experiment, thevalues of feature is binary such that are specified by the symbol representing thefeature and a symbol indicating absence of the feature.In Figure 3.6 w 1 , w 2 , ..., and w m indicate the top m words in frequent wordsranking in the dataset, and w 1,m+1 , w 2,m+2 w 7,m+n the n words at the end ofeach sentence. The ’nil’ indicates that those features are not included in thesentence.As the diagram indicates, the feature columns can be divided into severalgroups of columns and some of groups were exploited in combination. Correspondingly,sentences used as contexts of a targeted sentence for chunking canbe selected as same manner. The contexts using in this experiment are only consideredin units of sentence, thus we use the idea of “window” of a sequence ofsentences as exploited contexts for chunking. The window size varied from onlytarget sentence for chunking through three sentences including one forward andone backward sentence, and to another five sentences including two forward andtwo backward sentences of the target. Table 3.6 summarizes these experimentalconditions.39
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 and 44: this thesis. This Chapter proposes
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: Table 3.5. Transition of Question T
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?