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- しゅんすけ このえ
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1 Trial for Value Quantification from Exceptional Utterances
2
3 w α β
4 70 A 70 A A A B before after before before
5 w A.1w = w A.1w = w A.1w = w A.2w = w A.2w = w A.2w = w A.3w = w A.3w = w A.3w = α, β A.1 α = 0.3, β = α, β A.1 α = 0.2, β = α, β A.1 α = 0.3, β = α, β A.2 α = 0.3, β = α, β A.2 α = 0.2, β = α, β A.2 α = 0.3, β = α, β A.3 α = 0.3, β = α, β A.3 α = 0.2, β = α, β A.3 α = 0.3, β = A A TFIDF IDM
6 1 1.1 [1][2] [3] [4][5] 5
7 [6][7] [8] [9][10] Web Intelligence Web (Information Technology, IT) [11][12] [13] [14] [15] [16][17][18] or [19] 6
8 [20][21] [22][23] [24] [25] [26][27] [28][29] [30][31] [32][33] ( ) [34][35] [36][37] 7
9 1.2 8
10 2 [38] 1975 [39] [40][41] [42][43] KeyGraph[44] 2000 [45] [45] [46] [47] [48] [49] 2.1 [38] D D n t k (1 k n) 9
11 (1) D = (t 1, t 2,..., t n ) (1) (1) Information Retrieval IR IR (1) 2 D 1 D 2 (2) Sim(D 1, D 2 ) = D 1 D 2 D 1 D 2 (2) cos θ cos 1 cos 0 = 1 T 1 (2) D T 1, T2 T 3 =, 3 ( T, T T) D 2 = 1 2, 3 T 2 D = ( T, T T) 1 1 2, 3 T 3 1: 10
![2.2 [45] 2 Chance](/docs-images/91/105735840/images/12-0.jpg "favours the prepared")
12 2.2 [45] 2 Chance favours the prepared mind. 2: 11
13 2.3 IDM [49] IDM IDM [50][51] IDM (3) A(t) t C I A(t 1) A(t) R R i j R i j w i w j 0 γ α A(t) = C + ((1 γ)i + αr)a(t 1) (3) IDM Priming Effect [52] 12
14 : 13
15 : 14
16 : 15
![3.2 3.2.1 [53] 6](/docs-images/91/105735840/images/17-0.jpg "6: - 3.2.2?? 1: 16")
17 [53] 6 6: ?? 1: 16
18 UniqueT erm = {,,,,,,,,,,,,,,,,,,,,,,,, } , U 0 = {1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, C U 1 = {0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, B U 2 = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0}, C U 3 = {0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0}, A U 4 = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0}, B U 5 = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1}, C U 6 = {0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0}, A 17
19 (1) (2) cos before S before n = Sim(U k, W before n ) (4) after S after n = Sim(U k, W after n ) (5) n t W before n,w after n w (6) W before n = U k, W after n = U k (6) n w k<n n<k n+w w (7) S before n < α S after n < β (7) (7) 18
20 3.3.3 A w = 20, α = 0.2β = : No. before after 32 Z W K Z P P P Z W K Z W K ,2 19
21 , : 20
22 (8) P Uk t i U k t i E AUk = P Uk t i I Ati (8) t i U k I Ati A t i (9) P Ati A t i I Ati = log P Ati (9) before after P Uk t i U k t i I Wbefore k t i, I Wafter k t i W before k,wafter k t i before E beforeuk = P Uk t i I Wbefore k t i (10) t i U k after E afteruk = P Uk t i I Wafter k t i (11) t i U k W before k, W after k w (12) W before n = U k, W after n = U k (12) n w<k n n k<n+w w w 21
23 before before before after 8: before after after 9: after 22
24 (13) Ē = E beforeuk E afteruk (13) Ē E beforeu k E afteruk Ē E beforeu k E afteruk A.1 (13) before before Ē before Ē Ē
25 10: before - 3: Ē N Z K P I P
26 before : Ē Ē 11: before - 25
27 3.5 5 (13) 12 5: No. before after. 12: 26
28 : 4.2 w α β A.1 10 A
29 w w 3 w 6-14 α = 0.2 β = A.1 w = 20 A.2 w = 20 A.3 28
30 w = 30 w w TextTiling [54][55] HMM [56] : w A.1 w = 10 1 Z I Z Z K W Z W P K W P
31 7: w A.1 w = 20 1 K P P Z I P P P Z W W W Z : w A.1 w = 30 1 K Z P P K N N Z P P : w A.2 w = 10 1 Y : w A.2 w = 20 1 Y X T Y X
32 11: w A.2 w = 30 1 T T T Y T Y X K Y : w A.3 w = 10 1 DAN DAN : w A.3 w = 20 1 M EV DAN DAN : w A.3 w = 30 1 DAN DAN DAN
33 4.2.2 α β α β w α, β w w w before after α β after before α, β w α β α = 0.2, β = A.1 w = 20, α = 0.2, β = 0.3 A.2 w = 20, α = 0.2, β = 0.3 A.3 w = 30, α = 0.3, β =
34 15: α, β A.1 α = 0.3, β = K P P Z I P P P Z W W W Z : α, β A.1 α = 0.2, β = K P P Z I P P P Z W W W Z
35 17: α, β A.1 α = 0.3, β = P Z I Z W W Z W P N P K Z K W : α, β A.2 α = 0.3, β = Y T T T T Y K G X : α, β A.2 α = 0.2, β = Y T Y K X
36 20: α, β A.2 α = 0.3, β = Y T X Y G Y : α, β A.3 α = 0.3, β = DAN DAN DAN DAN j DAN DAN : α, β A.3 α = 0.2, β = DAN DAN DAN : α, β A.3 α = 0.3, β = DAN DAN
37 A.1 A
38 w = 20, α = 0.2, β = N 3 Z 37
39 5.2 2 w = 20, α = 0.2, β = Y
40 5.3 3 w = 30, α = 0.3, β = DAN 39
41
42 A.2 No A : A.2 No 1 66 Y 2 77 T 3 79 Y 4 75 K 5 59 X 25: A.3 No 1 30 DAN 2 51 DAN 3 37 DAN 4 33 DAN 5 45 DAN 41
43
44 w = 10, α = 0.2, β = % 43
45 w = 20, α = 0.2, β =
46
47 B ICD 1 ( ) w = 10 α = 0.2, β =
48 : ICD
49
50 1 1 49
51 2 2 N 50
52 IH M 51
53 ICD 2 ICD 2 ICD 2 ICD 52
54 2 1 L
55
56 6.2.5 TF-IDF [59] IDM [49] TF-IDF TF-IDF(Term Frequency and Inverse Document Frequency) 14 Information Retreval:IR t T F (t) t DF (t) t T F IDF (t) = T F (t) log 1 DF (t) (14) DF DF TFIDF TFIDF TFIDF 55
57 27: TFIDF IDM IDM IDM 2.3 IDM 28 IDM IDM IDM 56
58 28: IDM
59 6.2.6 w = 50, α = β = 1 w w : IDM 28 IDM 58
60 ( 29) 50 w 59
61
62 7 61
63 1 PC 62
64 2 1 NEC 63
65
66 [1], (1),, 15, pp , [2] web URL c40804/dzemi2000/kc799.html. [3],,, [4],, VOL.63,, [5],,, 12,,, 6, pp.65-76, [6],,,,, [7],,,, [8],, 24,, [9],,, Vol.10, No.4, pp , [10],,,,,, 17, 3 A, [11] Ning Zhong, Y. Yao, Web Intelligence(WI), Proceedings 24th Annual Computer Software and Applications Conference. COMPSAC2000, [12], WWW,, Vol.17, No.3, pp , [13],,, HIP,,Vol.99, Issue.451,pp.37-42, [14],,,, Vol.2003, p.269,
67 [15],,,, Vol.12,No.2, pp , [16] Saurabh Sinha, Mary J. Harrold, Analysis and Testing of Programs with Exception Handling Constructs, IEEE TRANSSACTION ON SOFTWARE, EN- GINEERING, Vol.26 No.9, pp , [17] Flaviu Cristian, Exception Handling and Sftware Fault Tolerance, IEEE TRANSACTION ON COMPUTERS, Vol.C-31, No.6, [18] Claus Hagen, and Gustavo Alonso, Exception Handling in Workflow Management Systems, IEEE TRANSACTION ON ENGINEERING, Vol.26, No.10, [19],,,, [20],,, vol.12, No.4, pp , [21] C.C. Aggarwal and P.S. Yu, Outlier Detection for High Dimensional Data, Proc. ACM SIGMOD 2001, May,Santa Barbara, pp.37-46, [22],,,, [23],, [24],,, Vol.18,page 33, [25],,, Vol.71, No.3, pp , [26], 5,,, 6, pp.31-39, [27], 5,, 6, pp.41-46, [28], / (II-7 ),, No.55, pp ,
68 [29],, 46 5, pp.1-33, [30],,, Vol.20 pp.1-24, [31], - -,, vol.4, issue.1, pp.109, [32],,,,,,, Vol.13, No.1, pp.62-79, [33],,,,,, -, 7, pp.67-70, [34],,, [35],,, Vol.1I37-10, pp.65-72, [36],,, Vol.40, No.11, pp , [37],,, Vol.44, No.1, pp.48-57, [38] G. Salton, A. Wong, and C. S. Yang, A Vector Space Model for Automatic Indexing, Communication of the ACM, Vol.18, No.11, pp , [39] G. Salton and M.J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, [40] R. Barzilay, M. Elhadad, Using lexical chains for text summarization, Advances in Automatic Text Summarization, pp.1-12, The MIT Press, London, [41],,,, Vol.J90-D No.2 pp ,2007. [42],,,,, D Vol.J78-D2, No.3, pp ,
69 [43] Support Vector Machine. NL-155 pp [44], N. E. Benson,, KeyGraph,, J82-D-1, No.2, pp , [45],,, [46] 6, -,, 19, 3, [47],,,,, 21, 3 H, [48],,,,, 16, 2, pp , [49],,,, 17 4 F, [50] M.R. Quillian, Semantic Memory, Semantic information processing, MIT Press, pp , [51] A.M. Collins and E.F. Loftus, A Spreading Activation Theory of Semantic Processing, Psychological Review, 82, pp , [52] R.F. Lorch, Priming and searching processes in semantic memory, A test of three models of spreading activation, Journal of Verbal Learning and Verbal Behavior, 21, pp , [53] ver [54] M.A.Hearst, TextTiling: Segmenting Text into multi-paragraph Subtopic Passages, Computational Linguistics, Vol.3, Issue.1, pp.33-64, [55],,, 47(3), pp , [56],, 4, HMM,,, Vol.2007, issue76, pp.7-10,
70 [57] miyazaki/gikai.html/. [58] [59] Stephen Robertson, Understanding Inverse Document Frequency, Journal of Documentation 60 no. 5, pp ,
71 A A N P P I P K N K ( ) I. N P I K P K K P N W I K W I W 4 70
72 I. K P K ( ) W Z K P K W Z P N W K Z P N P K P W P K P W K W K N Z P K P W P P W P K ( ) P P 10 Z
73 W P Z W W Z N W K W Z P K Z W Z Z K W K P P W P K W N W Z W P I, N P K W I. I. W I Z P Z I W N 72
74 I. W W W N I K Z. P W W K W K W W K P W P K W K P P W W P K W N W K K P W K P W Z K K 73
75 A T T X N Y G X K G N T G X Y Y T G Y T T G X Y T T T G T Y T T K Y T 74
76 K G G Y K T N T N K G Y Y T Y K G Y X T Y G Y K X G Y T Y 5 K N Y T Y G T G T A 75
77 K Y A X Y X A T X Y G K T A G T Y G Y X T Y G T Y A X G X T G T T K T G T G Y G G Y Y G 76
78 G Y K G Y K Y T G K G T Y K G Y G Y G Y K X G T Y Y G T Y Y T G K A
79 14: D M DAN M M keygraph DAN DAN M DAN DAN D M M DAN M DAN DAN 1 DAN M D 11 M DAN M D
80 DAN DAN D D DAN 2008 D DAN DAN M DAN j D DAN M DAN M D DAN DAN M 2001 EV EV DAN EV EV M Electrical Vehcle DAN D Ero Voice DAN Ero M DV EV DAN 2008 D EV DAN D DAN D D Ero Voice DAN M Ero 79
81 DAN M DAN M EV M DAN DAN DAN Ero Voice DAN Ero M EV DAN D EV DAN M D DAN M D EV M DAN M DAN M DAN D M D D 80
82 B
2007/2 Vol. J90 D No Web 2. 1 [3] [2], [11] [18] [14] YELLOW [16] [8] tfidf [19] 2. 2 / 30% 90% [24] 2. 3 [4], [21] 428
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Informative Summarization Method by Key Sentences Extraction Considering Sub-Topics Naoki SAGARA, Wataru SUNAYAMA, and Masahiko YACHIDA 1. 1990 WWW World Wide Web Web [15] Graduate School of Engineering
1 AND TFIDF Web DFIWF Wikipedia Web Web 2. 3. 4. AND 5. Wikipedia AND 6. Wikipedia Web 7. 8. 2. Ma [4] Ma URL AND Tian [8] Tian Tian Web Cimiano [3] [
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DEIM Forum 2015 B1-5 606 8501 606 8501 E-mail: komurasaki@dl.kuis.kyoto-u.ac.jp, tajima@i.kyoto-u.ac.jp Web Web AND AND Web 1. Twitter Facebook SNS Web Web Web Web [5] Bollegala [2] Web Web 1 Google Microsoft
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14 A Method of Article Retrieval Utilizing Characteristics in Newspaper Articles 1055104 2003 1 31 1 1 tf-idf tf-idf i Abstract A Method of Article Retrieval Utilizing Characteristics in Newspaper Articles
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[II] Optimization Computation for 3-D Understanding of Images [II]: Ellipse Fitting 1. (1) 2. (2) (edge detection) (edge) (zero-crossing) Canny (Canny operator) (3) 1(a) [I] [II] [III] [IV ] E-mail sugaya@iim.ics.tut.ac.jp
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Title 中國宗教文獻研究國際シンポジウム報告書 ( 大規模佛教文獻群に對する確率統計的分析の試み / 師茂樹 ) Author(s) Citation (2004) Issue Date URL Right Typ
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No. 3 Oct The person to the left of the stool carried the traffic-cone towards the trash-can. α α β α α β α α β α Track2 Track3 Track1 Track0 1
ACL2013 TACL 1 ACL2013 Grounded Language Learning from Video Described with Sentences (Yu and Siskind 2013) TACL Transactions of the Association for Computational Linguistics What Makes Writing Great?
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The Society for Economic Studies The University of Kitakyushu Working Paper Series No. 2011-12 (accepted in March 30, 2012) () (2009b) 19 (2003) 1980 PC 1990 (, 2009) (2001) (2004) KH coder (2009) TinyTextMiner
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Vol.89 No.3 298-299 Application of Statistical Process Control to Software Development Mutsumi Komuro 1 23 1985 ACM IEEE 1 195QC Quality Control 1 2 CMM Capability Maturity Model CMMI Capability Maturity
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ol2013-nl-214 No6 1,a) 2,b) n-gram 1 M [1] (TG: Tree ubstitution Grammar) [2], [3] TG TG 1 2 a) ohno@ilabdoshishaacjp b) khatano@maildoshishaacjp [4], [5] [6] 2 Pitman-Yor 3 Pitman-Yor 1 21 Pitman-Yor
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Computational Semantics 1 category specificity Warrington (1975); Warrington & Shallice (1979, 1984) 2 basic level superiority 3 super-ordinate catego
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TF-IDF TDF-IDF TDF-IDF Extracting Impression of Sightseeing Spots from Blogs for Supporting Selection of Spots to Visit in Travel Sat
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14 2 5 i ii Surface Reconstruction from Point Cloud of Human Body in Arbitrary Postures Isao MORO Abstract We propose a method for surface reconstruction from point cloud of human body in arbitrary postures.
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