SIG-AM [ 99] [Ramachandran 01] sound symbolism [Hinton 95] [ 06] Ueda et al.[ueda 12] I [ 93] SVM [ 12, Aramaki 12] SVM 3 Twitter
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1 SIG-AM Which sense does an onomatopoeia belong to? 1 1 1,2 Tetsuaki Nakamura 1 Mai Miyabe 1 Eiji Aramaki 1,2 1 1 Unit of Design, Center for the Promotion of Interdisciplinary Education and Research, Kyoto University 2 2 JST PRESTO Abstract: This study aims to develop a system which visualizes subjective information. Focusing on onomatopoeias as such information, we estimate which senses an onomatopoeia belongs to among touch, taste, smell, hearing, sight, pleasure (positive) and unpleasure (negative). For this purpose, we use a machine learning method (Support Vector Machine) which utilizes phonetic symbols and the number of occurrences of them in the onomatopoeia. Then, the experimental result for evaluation demonstrates that (1) the best performance is achieved for hearing and sight, and (2) the performance of the classifier is similar to that of human. Finally, we propose the system which creates city maps displaying distribution of subjective information for senses. 1 [Houghton 12] [ 11] [Suzuki 11] tetsuakinakamura8@gmail.com 1 kyoshinmonitor.html Support Vector Machine SVM [Cortes 95] - 1 -
2 SIG-AM [ 99] [Ramachandran 01] sound symbolism [Hinton 95] [ 06] Ueda et al.[ueda 12] I [ 93] SVM [ 12, Aramaki 12] SVM 3 Twitter ,817, [ 06, Ueda 12] : : a y i r u w e N o Q k R s D t P n Y h W m v todkitodki kyunkyun kirkir hopahopa [ 99] $ bigram trigram
3 SIG-AM : Wa va vi vu ve vo a i u e o ka ki ku ke ko sa si su se so ta ti tu te to na ni nu ne no ha hi hu he ho ma mi mu me mo ya yu yo ra ri ru re ro wa wo N Q R A I U E O Ya Yu Yo D P A I U E O 4.2 SVM TinySVM 3 7 SVM / A-B B-C C-A κ κ κ 3 taku/software/tinysvm/ 4: κ ( )0.4 κ w s l(w, s) 3 1 l(w, s) = { +1 (x(w, s) 2) 1 (x(w, s) = 0) (1) 1 x(w, s) w s x(w, s) =
4 SIG-AM : : SVM 10 F κ / / / / / /342 1 κ 4 κ SVM SVM κ F Pearson x F y x y s1-4 -
5 SIG-AM : SVM /SVM / / / / (4448) (7966) (7721) (5174) (3463) (740) (3933) (1303) (3450) (711) (2898) (1248) (1986) (700) (2697) (989) (1890) (655) (2638) (748) (5241) (1806) (7721) (4448) (3933) (1528) (5174) (1890) (3463) (704) (2898) (1303) (2697) (599) (2253) (900) (2482) (498) (2230) (816) (2898) (5174) (7966) (5241) (2697) (4448) (1100) (1044) (2570) (3450) (775) (1038) (2456) (2482) (748) (918) (2253) (1086) (717) (649) (1806) (8353) (10968) (2253) (1044) (7721) (9625) (540) (719) (5174) (7966) (469) (542) (2570) (4448) (420) (528) (1890) (3450) (397) (10968) (2898) (7721) (2570) (9625) (2697) (5174) (1665) (7966) (2253) (2482) (1303) (5241) (1086) (2217) (1181) (3933) (1077) (1890) (668) 1 / / 2 3 Twitter 8: Pearson p s2 / s1 s2 κ 9 9 κ A D A. B. C. D
6 SIG-AM : κ : κ SVM κ A i.e., B C D synesthetic metaphor / synaesthetic metaphor [Ullmann 51, Williams 76, Yu 03, Werning 06, 94] [ 12] A D κ Twitter 1 7 SVM SVM SVM JST - 6 -
7 SIG-AM a + b + c + d + Twitter % 10% 70% 1: - 7 -
8 SIG-AM [Aramaki 12] Aramaki, E., Yasuda, S., Miyabe, M., Miura, S., and Murata, M.: Which is Stronger? : Discriminative Learning of Sound Symbolism, in Proceedings of the 34th Annual Meeting of the Cognitive Science Society (CogSci2012), p (2012) [Cortes 95] Cortes, C. and Vapnik, V.: Support- Vector Networks, Machine Learning, Vol. 20, No. 3, pp (1995) [ 06],,, 2,, Vol. 62, No. 11, pp (2006) [Hinton 95] Hinton, L., Nichols, J., and Ohala, J. J. eds.: Sound Symbolism, Cambridge University Press, Cambridge (1995) [Houghton 12] Houghton, A., Prudent, N., Scott III, J. E., Wade, R., and Luber, G.: Climate Change-Related Vulnerabilities and Local Environmental Public Health Tracking through GEMSS: A Web-Based Visualization Tool, Applied Geography, Vol. 33, pp (2012) [ 94],, (1994) Communications Technology, Vol. 58, No. 3, pp (2011) [ 99],,, (1999) [Ueda 12] Ueda, Y., Shimizu, Y., and Sakamoto, M.: System Construction Supporting Communication with Foreign Doctors Using Onomatopoeia Expressing Pains, in Proceedings of the 6th International Conference of Soft Computing and Intelligent System, pp (2012) [Ullmann 51] Ullmann, S.: The principles of semantics, Blackwell, Oxford (1951) [Werning 06] Werning, M., Fleischhauer, J., and Beseoglu, H.: The cognitive accessibility of synaesthetic metaphors, in Proceedings of the 28th Annual Conference of the Cognitive Science Society, pp (2006) [Williams 76] Williams, J. M.: Synaesthetic adjectives: A possible law of semantic change, Language, Vol. 52, No. 2, pp (1976) [Yu 03] Yu, N.: Synesthetic metaphor: a cognitive perspective, Journal of literaty semantics, Vol. 32, No. 1, pp (2003) [ 12],,,,, 18, pp (2012) [ 93],, Vol. 49, No. 9, pp (1993) [ 12],,,, Vol. 19, No. 3, pp (2012) [Ramachandran 01] Ramachandran, V. S. and Hubbard, E. M.: Synaesthesia: A window into perception, thought and language, Journal of Consciousness Studies, Vol. 8, No. 12, pp (2001) [ 11],,, (VENUS),, Vol. 46, No. 1, pp (2011) [Suzuki 11] Suzuki, M. and Inoue, D.: DAEDALUS: Practical Alert System Based on Large-scale Darknet Monitoring for Protecting Live Networks, Journal of the National Institute of Information and - 8 -
9 SIG-AM Analysis of User s Behavior in Information Retrieval Using Search Engine Shogo Kori 1, Yu Kato 1, Yasufumi Takama Graduate School of System Design, Tokyo Metropolitan University Abstract: 1 Web Web 20 Web Web [8][10] kori-shogo@sd.tmu.ac.jp Web Web 20 Web Web Web - 9 -
10 SIG-AM [4] WWW Web Web Tips [6][7] [5] [6] [6] Google Trends 1 Yahoo! MySQL Web Ruby on Rails : 2.2 [3] SVM [9] Web [1] ipod ipod ipod Web
![人工知能学会 インタラクティブ 情報アクセスと可視化マイニング研究会(第4回) SIG-AM-04-02 ユーザ行動の分析 を目的とする Navigational に分類できる [2] さらに Discover-Informational には 正確に目標を定めた検索 検索意図の観点からユーザの情報検索行動を調査す Pinpoint と幅広い検索結果を期待した検索 Broad る実験を行った 3](/docs-images/93/111470272/images/11-0.jpg "節で行った実験の概要および そ が存在する Discover-Informational-Pinpoint には条 の結果を分析し検索意図を分類した結果について示す 件を満たす情報を一つだけ探す検索 Single と複数の 4 節では 3 節で定めた分析意図によりログデータにラ 情報を探す検索 Multi があり Multi にはそれらが ベル付けを行い分析を行った結果を示すと共に 構築")
11 人工知能学会 インタラクティブ 情報アクセスと可視化マイニング研究会(第4回) SIG-AM ユーザ行動の分析 を目的とする Navigational に分類できる [2] さらに Discover-Informational には 正確に目標を定めた検索 検索意図の観点からユーザの情報検索行動を調査す Pinpoint と幅広い検索結果を期待した検索 Broad る実験を行った 3 節で行った実験の概要および そ が存在する Discover-Informational-Pinpoint には条 の結果を分析し検索意図を分類した結果について示す 件を満たす情報を一つだけ探す検索 Single と複数の 4 節では 3 節で定めた分析意図によりログデータにラ 情報を探す検索 Multi があり Multi にはそれらが ベル付けを行い分析を行った結果を示すと共に 構築 一覧のようにまとめられている Web ページを期待した 中の検索エンジンに必要な基本検索機能について考察 検索 List と一つずつ別ページに存在することを期待 する した検索 Item が存在する 同じクエリであっても 検索される段階によって検索意図が異なると考えられ る場合があった 3.1 ユーザ行動調査のための実験 今回の実験で入力されたクエリの例を以下に示す 今 回の実験では Item に該当する検索は行われなかった 実験で用いた問題を図 2 に示す 実験では 二枚の 画像から検索エンジン Google を用いて画像の撮影 Verify-Informational 場所を特定する問題を出題した 図 2 の問題のように 市場 スペイン バルセロナ 答えをどのような視点から探せばよいか 画像をクエ 写真がバルセロナ スペイン の市場で撮影し リとしてどの様に表現すればよいかが明確ではない場 たものであることを確認 合 実験協力者は自ら解答への道筋を考えなければな らない 答えを見つけるアプローチの仕方が様々であ Verify-Navigational り 多様な検索行動が生じることが期待できるためこ サン ジョセップ市場 Google マップ の問題を選択した 実験は実験協力者 3 名を対象に行 サン ジョセップ市場の場所を Google マップ い 各協力者は平均して約 20 分で正解を出すことがで で確認 きた 実験協力者がどのような検索を行い どのよう Discover-Navigational なページを開いているかを正確に調査するため実験中 バルセロナ wiki に ocam5 を用いて画面のキャプチャを行った バルセロナについて書かれている Wikipedia の ページを期待 3 Broad ヨーロッパ 市場 ヨーロッパにある市場について幅広い情報を 期待 Single サン ジョセップ市場 住所 サン ジョセップ市場の住所が書かれている Web ページを期待 List スペイン 市場 一覧 スペインの市場が一覧のようにまとめられてい る Web ページを期待 図 2: 実験で用いた問題 3.2 検索意図の分析 入力されたクエリを分析し 検索意図を図 3 の様 に分類した 実験協力者の検索意図は Verify 検証 と Discover 発見 の二つのタイプに大別される ま た 何かに関する情報を探す際には 対象ページを限 定しない Informational と 特定の Web ページの発見
12 SIG-AM : 3: 2: F A H Discover Navigational Broad List Verify Navigational Informational Discover Pinpoint Single Multi 3 Pinpoint Single/Multi/List Verify-Informational Verify 3 F 2 H Pinpoint Single/Multi/List Verify- Informational Verify Multi Single 3: H
13 SIG-AM Web Navigational Informational Navigational Pinpoint Broad Pinpoint-Single Pinpoint-Multi Informational-Pinpoint-Multi 2013 Informational-Pinpoint-Single Informational-Broad 2013 Navigational-Pinpoint-Multi Navigational-Pinpoint-Single Navigational-Broad 4: API [1] Web DEIM Forum A7-2, 2009 [2] C. D. Manning, P. Raghavan, H. Schutze: Ch. 19: Web search basics, Introduction to Information Retrieval, Cambridge University Press, [3] DEIM Forum 2012, A4-4,
14 SIG-AM [4] WWW Vol.102, No617, pp.59-64, 2007 [5] 2004(108), pp.88-94, 2004 [6] 3 pp [7] Web FSS2012, pp , 2012 [8] Vol.43, No.6, pp , 2002 [9], HCI-142(8), pp.1-6, 2011 [10] DAISEN 1 2 pp.11-12,
15 SIG-AM Log Data Analysis on Interacitve Information Access 1 Tsuneaki Kato The University of Tokyo Abstract: The characteristics of user behaviors in explorative information access are reported, which reflect the di erences of the environments she uses and the tasks she engages in. Using a model of information access behaviors and a log data coding based on that model, the analysis was conducted on the log data obtained in VisEx, an experiment for evaluating interactive and explorative information access environments. It shows that introduced retrieval methods, narrowing-down and similarity-based retrieval, are used as a substitute of sequential document checking, and those e ectiveness di ers depending on task characteristics. 1 VisEx[3] 1 VisEx kato@boz.c.u-tokyo.ac.jp 1 VisEx VisEx VisEx NTCIR VisEx
16 SIG-AM T : VisEx Web Web Web [4] (1) (2) 2.2 VisEx TREC Interactive track[2] E1: E2: T1: T2: E VisEx 4 (BL) UTLIS (UT) BL Web UT BL [5] [6] UT Web Bate [1] 3 Web SERP
17 SIG-AM back
18 SIG-AM KW / Web 3.3 URL 3 URL 10 / / /
19 SIG-AM : KW KW ,2 IQR 2 KW 10 KW ,4 10 KW ,6 UT t(26.242) = 5.53, p < ,8 2 IQR UT KW 10 KW UT UT 4.2 KW 10 KW KW
20 SIG-AM : (IQR) F(1,76) (p ) BL UT : KW 15.5(18.5) 12.5(11) 3.5(5.5) 8(6.25) 21.51(<.001) 0.00(.96) 3.48(.07) (29.75) 6(13.5) 14(24.75) 4.5(9.25) 2.75(.10) 13.94(<.001) 0.50(.48) KW * 15.5(18.5) 12(9.5) 3.5(5.5) 8(6.25) 20.92(<.001) 0.10(.76) 5.69(.02) 10 * 35.5(29.75) 6(12.5) 12(24) 4.5(9.25) 6.41(<.01) 22.98(<.001) 4.94(.03) 8(4.25) 0(1.25) 11(12.5) 12(10.5) 15.5(6.25) 10.5(8.25) 13(2.75) 10.5(4.25) 4.44(.04) 10.48(<.01) 0.45(.51) 26.5(13.25) 9.5(9.0) 17(6.5) 8(9.5) 10.01(<.01) 46.91(<.001) 5.76(.02) KW * 10 * 2: (IQR) F(1,76) (p ) BL UT : 868.5(333) 708.5(418) (464) 634.5(313) 0.00(.99) 12.85(<.001) 0.76(.39) 210(191) 255(174) 212(101) 165(110) 1.99(.16) 1.08(.30) 2.12(.15) 263.5(215) 170(169) 195 (155) 126.5(142) 0.44(.51) 12.38(<.001) 0.42(.52) (614) 1877(914) 1423(443) 1917(334) 0.05(.82) 13.92(<.001) 0.06(.81) 3: KW E1 KW KW KW KW KW 3 UT KW KW UT IQR 4 BL 10 BL UT KW UT KW KW KW UT KW KW
21 SIG-AM : KW KW (IQR) F(1,76) (p ) BL UT : KW 3.5(7.5) 3(4) 2(3) 4.5(5.5) 1.62(.21) 0.03(.87) 8.8(<.01) KW 9(10.5) 6(11.25) 0.5(2.25) 2(4.75) 25.70(<.001) 0.18(.67) 0.48(.49) 4: (IQR) BL UT KW 3 (7.25) 7.5 (5.25) 0 (1) 2.5 (3.25) (8.5) 3.5 (5.25) 2 (6.25) 1 (2) 1.5 (2.25) 0 (1) 0 (0.25) 0 (1) 5.5 (7) 0 (2) 1.5 (4.5) 5.5 (8.5) KW BL KW KW UT KW UT KW BL KW KW KW BL KW KW BL KW KW KW UT KW KW KW KW
22 SIG-AM VisEx VisEx [7] VisEx VisEx NTCIR VisEx (B) [1] M.J. Bates: The Design of Browsing and Berrypicking Techniques for the Online Search Interface. in Online Information Review, Vol. 13, No. 5, pp , [2] S.T. Dumais and N.J. Belkin: The TREC Interactive Tracks: Putting the User into Search. in E.M. Voorhees and D.K. Harman ed. TREC Experiment and Evaluation in Information Retrieval, pp , The MIT Press, [3] : VisEx., [4] : NTCIR-9 VisEx., [5] :. VisEx, [6] B. Shneiderman: Dynamic Queries for Visual Information Seeking. in IEEE Software Vol.11, No.6., pp , [7] R.W. White and J. Huang: Assessing the Scenic Route: Measuring the Value of Search Trails in Web Logs. in The Procs. of SIGIR 10, pp ,
23 SIG-AM Latent Topic-based Graph Construction for Text Classification 1 1 Akiko Eriguchi 1 Ichiro Kobayashi Advanced Sciences, Graduate School of Humanities and Sciences, Ochanomizu University Abstract: This paper aims to raise the accuracy of multi-class text classification by means of graph-based semi-supervised learning (GBSSL). It is essential to construct a proper graph expressing the relation among nodes in GBSSL. We propose a method to construct a similarity graph by employing both surface information and latent information to express similarity between nodes. Experimenting on Reuters corpus, we have confirmed that our proposed method works well for raising the accuracy of GBSSL in multi-class text classification task. 1 (Semi-Supervised Learning: SSL) (Graph-Based Semi-Supervised Learning: GBSSL) Support Vector Machine (SVM)[2] [4] GBSSL ( ) [7, 9] GBSSL [7, 10] [9] GBSSL k- k- GBSSL [11] [11, 12] g @is.ocha.ac.jp GBSSL (1 α) : α (0 α 1) α PRBEP 2 GBSSL GBSSL 2.1 ( ) G = (V, E)
24 SIG-AM V E G W w ij W i j GBSSL i k- K(i) w ij = sim(x i, x j ) δ(j K(i)) δ(z) z tfidf [3] (sim surface ) tfidf ( ) (sim latent ) ( (4))) L2 ( (5)) Latent Dirichlet Allocation (LDA) [1] (sim surface ) (sim latent ) α (0 α 1) sim latent sim surface α : (1 α) (0 α 1) ( S T ) (sim nodes ) ( (1)) P Q S T sim nodes (S, T ) α sim latent (P, Q) +(1 α) sim surface (S, T ) (1) sim surface (S, T ) = cos(tfidf(s), tfidf(t )) (2) sim latent (P, Q) = exp L2 (P,Q) (3) σ 1 (x) = exp x (4) L 2 (P, Q) = (P (x) Q(x)) 2 dx (5) 2.3 GBSSL [5, 8] ( ) W n ( l ) n f ( (6)) ( (8)) (6) 1 2 λ(> 0) (6) L (7) L( D W ) D W ( ) J(f) = l (y (i) f (i) ) 2 i=1 +λ i<j w (i,j) (f (i) f (j) ) 2 (6) = y f λf T Lf (7) f = (I + λl) 1 y (8) Reuters (Reuters) 1 Reuters 135 Reuters newswire ModApte GBSSL Subramanya [4] 10 earn acq money-fx grain crude trade interest ship wheat corn Reuters one-versus-rest ( )u = 3299 l = n = (others) reuters21578/
25 SIG-AM l α = 0 α 0 5 α [0, 1] 0.1 V = n(= 3319) k- k {2, 10, 50, 100, 250, 500, 1000, 2000, n} λ {1, 0.1, 0.01, 1e 4, 1e 8} 15 5 (k, λ) 10 PRBEP PRBEP PRBEP P recision( ) Recall( ) 3.2 [0, 1] 0.1 α (k, λ) α PRBEP 1 10 α 10 PRBEP α PRBEP α = 0, 0.2, 1 PRBEP PRBEP α = 0, 0.2, α = 0 α = 1 (α 0 1) (α : (1 α)) 1 10 α = 0 α 0 PRBEP 1, 2, 3, 6, 7, 8 α = 0 α 0 PRBEP α 4, 5, 9, (α = 0.2) 44.5 (α = 1) α = (α = 0.2) α = 1 6.5% α = 0 5.9% α = ( ) α = 0.2 ( ) 12, 13 α = 0, 1 α = 0.2 α PRBEP α α ( ) α = 0 α 0 PRBEP α 11 PRBEP α = 1 α 0 α = 0 α α = 0.1, 0.2, 0.3, 0.4, 0.5 t 5% 12, 13 GBSSL wheat, corn α = 0 α = 1 PRBEP LDA GBSSL
26 SIG-AM : (k, λ) α earn (50, 1) (1000, 1) (1000, 1) (1000, 1) (1000, 1) (1000, 1) (1000, 1) (1000, 1) (1000, 1) (1000, 1) (1000, 1) acq (500, 0.1) (250, 0.1) (250, 0.01) (100, 0.01) (100, 1e-8) (50, 0.1) (10, 1e-8) (10, 1e-8) (10, 1e-4) (250, 0.01) (500, 1e-4) money-fx (2, 1) (2, 1) (10, 0.1) (2, 0.1) (2, 1) (2, 1) (50, 1e-4) (50, 0.01) (2, 1e-8) (50, 0.01) (10, 0.1) grain (100, 0.1) (50, 1) (50, 1) (10, 1) (50, 1e-8) (10, 1) (10, 1) (50, 1e-8) (50, 1e-8) (50, 1) (50, 1) crude (10, 1) (50, 0.1) (50, 0.01) (100, 1e-8) (10, 0.01) (10, 1e-8) (50, 1e-8) (2, 1e-4) (50, 1e-8) (2, 1e-8) (50, 1e-8) trade (10, 1) (10, 1e-8) (10, 1e-8) (10, 1e-4) (10, 1e-8) (10, 1e-4) (10, 1e-8) (2, 0.01) (10, 1e-8) (10, 1e-8) (10, 0.1) interest (10, 0.1) (10, 1) (10, 0.1) (10, 1e-8) (10, 1) (10, 1) (10, 1) (10, 1) (10, 1) (100, 1e-8) (100, 1e-8) ship (10, 1) (100, 1e-8) (50, 0.1) (10, 1e-8) (10, 0.1) (10, 0.1) (10, 0.1) (10, 0.1) (2, 1) (10, 0.1) (10, 0.1) wheat (100, 0.01) (100, 1e-8) (100, 1e-8) (50, 1e-4) (50, 1e-4) (50, 1e-4) (100, 1e-8) (50, 1e-8) (50, 1e-8) (50, 1e-8) (50, 1e-8) corn (10, 1) (10, 1) (10, 1) (10, 1) (10, 1) (10, 0.01) (10, 0.01) (10, 0.1) (10, 0.1) (2, 1e-8) (10, 1e-8) 1: earn PRBEP 2: acq PRBEP 3: money-fx PRBEP 4: grain PRBEP 5: crude PRBEP 6: trade PRBEP 7: interest PRBEP 8: ship PRBEP 9: wheat PRBEP
27 SIG-AM : corn PRBEP 11: PRBEP 12: PRBEP α = 0, 0.2, 1 earn acq money-fx ( ) PRBEP( ) 13: PRBEP α = 0, 0.2, 1 money-fx grain crude trade interest ship wheat corn ( ) PRBEP( )
28 SIG-AM GBSSL α 5 Reuters GBSSL GBSSL ( ) [1] Blei, D. M., Ng, A. Y., Jordan, M. I.: Latent dirichlet allocation, Journal of Machine Learning Research (2003) [7] Zhu, X., Ghahramani, Z., Lafferty, J.: Semi- Supervised Learning Using Gaussian Fields and Harmonic Functions, in Proc. of the International Conference on Machine Learning (ICML) (2003) [8] Zhu, X., Ghahramani, Z., Lafferty, J. Semisupervised learning using Gaussian fields and harmonic functions, In ICML (2003) [9] Zhu, X.: Semi-Supervised Learning with Graphs, PhD thesis, Carnegie Mellon University (2005) [10] Gu, Q. and Han, J.: Towards Active Learning on Graphs: An Error Bound Minimization Approach, Data Mining, IEEE International Conference (2012) [11] Ozaki, K., Shimbo, M., Komachi, M. and Matsumoto, Y.: Using the mutual k-nearest neighbor graphs for semi-supervised classification of natural language Data, Proceedings of the Fifteenth Conference on Computational Natural Language Learning (2011) [12] Jebara, T., Wang, J. and Chang, S.: Graph construction and b-matching for semi-supervised learning, Proceedings of the 26th Annual International Conference on Machine Learning (2009) [2] Cortes, C., Vapnik, V.: Support-vector networks, Machine Learning,20: (1995) [3] Salton, G., McGill, J.: Introduction to Modern Information Retrieval, McGraw-Hill (1983) [4] Subramanya, A., Bilmes, J.: Soft-Supervised Learning for Text Classification, in Proc. of the 2008 Conference on Empirical Methods in Natural Language Processing, pp (2008) [5] Zhou, D., Bousquet, O., Lal, T. N., Weston J., Schölkopf B.: Learning with Local and Global Consistency, in NIPS 16 (2004) [6] Zhu, X., Ghahramani, Z.: Learning from Labeled and Unlabeled Data with Label Propagation, Technical report, Carnegie Mellon University (2002)
29 SIG-AM Validation on Efficient Text Classification Based on Latent Semantic with a Graph of Co-occurring Terms Yukari Ogura Ichiro Kobayashi Advanced Sciences, Graduate School of Humanities and Sciences, Ochanomizu University Abstract: We have proposed a method to raise the accuracy of text classification based on latent topic information, introducing several techniques such as extracting important words with PageRank algorithm and reducing the size of target documents by replacing them with important sentences in themselves. We have experimented on text classification with Reuters data set and confirmed that our proposed method worked to raise the accuracy of text classification. In this paper, we aim to verify our method with additional experiments using 20 Newsgroups data set and report the experimental result. 1 [13] tf idf PageRank k-means Reuters Newsgroups g @is.ocha.ac.jp 2 Hassan [1] n- PageRank Zaiane [2] Wang [3] Wang [3] PageRank PageRank Kubek [4] PageRank Erkan [5] LexRank TextRank PageRank PageRank [13] Newman [8]
30 SIG-AM PageRank [13] PageRank PageRank Brin [6] Web PageRank 1 V a V b V a V b Web Web PageRank Web G = (V, E) In(V a ) V a Out(V a ), V a V a PageRank (1) PageRank d (dumping factor) [0, 1] S(V a ) = (1 d) N + d V b In(V a ) 1: S(V b ) Out(V b ) (1) PageRank Web PageRank tf idf [3][1][10] Hassan [1] PageRank tf idf PageRank 3.2 Latent Dirichlet Allocation(LDA)[7] step1 (PMI:Point-wise Mutual Information) step2 step1 PMI 2 PMI Newman [8] PageRank
31 SIG-AM : step3 step2 step4 LDA Jensen- Shannon k-means Reuters Newsgroups Reuters [9] [11] 10 acq corn crude earn grain interest money-fx ship trade wheat , Newsgroups 20 11,269 53,975 [12] jason/20newsgroups/ comp.graphics rec.sport.baseball sci.space talk.politics.mideast ,198 4-News LDA α = 0.5 β = k-means 4.2 [9] F 2 d i l i d i α i d i (2) n i=1 = δ (map (l i), α i ) (2) n δ (x, y) x = y 1 0 map (l i ) k-means d i F c i F P (c i ) R (c i ) (3) F (c i ) = 2 P (c i) R (c i ) P (c i ) + R (c i ) (3) F ( (3)) ( (4)) F = 1 C F (c i ) (4) c i C k-means 1 k
32 SIG-AM k-means 10 LDA θ 1 θ k-means 10 Jensen-Shanon 3 F F 5 6 1: Reuters News PageRank tf idf : F Reuters News PageRank tf idf : Reuters PageRank 12,268 13,141 13,589 13,738 13,895 tf idf 13,999 14,573 14,446 14,675 14, News Reuters Reuters News 4-News 4: 4-News PageRank 10,731 10,958 11,078 11,171 11,241 tf idf 11,048 11,441 11,731 11,849 11,937 5: Reuters News PageRank tf idf Reuters LDA 3 Reuters News 6 Reuters News tf idf PageRank 3 PageRank 3 4 tf idf PageRank tf idf tf idf tf idf Reuters News Reuters News 10 Reuters News 4 4-News
33 SIG-AM : F Reuters News PageRank tf idf News 6 [13] PageRank,, 20 Newsgroups Reuters Newsgroups, LDA, k-means, [1] Samer Hassan, Rada Mihalcea, Carmen Banea.: Random-Walk Term Weighting for Improved Text Classification, (2007) [2] Osmar R.Zaiane, Maria-luiza Antonie.: Classifying Text Documents by Associating Terms with Text Categories, In Proc. of the Thirteenth Australasian Database Conference(ADC 02), pp [3] Wei Wang, Diep Bich Do, and Xuemin Lin.: Term Graph Model for Text Classification, Springer-Verlag Berlin Heidelberg 2005, pp (2005) [4] Mario Kubek, Herwig Unger.: Topic Detection Based on the PageRank s Clustering Property, IICS 11, pp (2011) [5] Gunes Erkan.: LexRank: Graph-based Lexical Centrality as Salience in Text Summarization, Journal of Artificial Intelligence Research 22, pp (2004) [6] Sergey Brin, Lawrence Page.: The Anatony of a Large-scale Hypertextual Web Search Engine, Computer Networks and ISDN Systems, pp (1998) [7] David M. Blei, Andrew Y. Ng, Michael I. Jordan, John Lafferty.: Latent dirichlet allocation, Journal of Machine Learning Research, Vol. 3, p (2003) [8] Newman David, Lau Jey Han, Grieser karl, Baldwin Timothy.: Automatic evaluation of topic coherence, Human Language Technologies :The 2010 Annual Conference of the North Ametican Chapter of the Association for Computational Linguistics, pp (2010) [9] Gunes Erkan.: Language Model-Based Document Clustering Using Random Walks, Association for Computational Linguistics, pp (2006) [10] Christian Scheible, Hinrich Shutze.: Bootstrapping Sentiment Labels For Unannotated Documents With Polarity PageRank, Proceedings of the Eight International Conference on Language Resources and Evaluation, (2012) [11] Amarnag Subramanya, Jeff Bilmes.: Soft- Supervised Learning for Text Classification, Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pp , Honolulu (2008) [12] Liping Jing, Michael K.Ng, Jun Xu, Joshua Zhexue Huamg.: Subspace Clustering of Text Documents with Feature Weighing K-Means Algorithm PAKDD 2005, LNAI 3518, pp (2005) [13] : 3,(2013)
34 SIG-AM TETDM Consideration of Design Guide for Constructing General Purpose System using TETDM Tomoki Kajinami 1 Koichi Tashiro 2 Takuma Tonegawa 2 Yuuya Kitamura 2 Yasufumi Takama Kanagwa Institute of Technology 2 2 Tokyo Metropolitan University Abstract: This paper considers a collaborative policy for combining tools, in development of system using TETDM. TETDM is an total environment for text data mining, can prepare for various mining tasks by combination of small mining tools. However, an useful guide in the design of system constructed with several small tools developed by different tool developers has not been considered. This paper describes a design guide adjusting user s purpose and system s specifications for constructing general purpose system, and shows an example of practice. 1 TETDM TETDM [6] TETDM 1 TETDM TETDM kajinami@ic.kanagawa-it.ac.jp TETDM 3 1. TETDM TETDM TETDM TETDM TETDM
35 SIG-AM TETDM 2 TETDM TETDM TETDM [7] R TETDM [8] TETDM TETDM TETDM [2][4] TETDM TETDM 2 TETDM 2.2 [3] [5] TETDM TETDM / /
36 SIG-AM [1] TETDM TETDM ID 3 TETDM 2 TF-IDF TETDM boolean double ID 1:. double[][] boolean[][] int[][] double[][] TETDM TETDM 2 2:. TF-IDF, BM25 K-means, ( )
37 SIG-AM TETDM 1 TETDM TETDM TETDM 1 1 [2][4] ( ) TETDM TETDM 1:. 2:. TETDM TETDM 1 1 TETDM
38 SIG-AM : TETDM TETDM 3 TFIDF BM25 K-means 6 TETDM TETDM TETDM
39 SIG-AM [1],, ( ), BP (1994) [2],, TETDM, 27, 3B3-NFC-01a-1 (2013) [3],,,,, Vol.47, No.SIG 19 TOD 32, pp (2006) [4], TETDM, 2, SIG-AM-02-10, pp (2012) [5],,, Vol DBS-148, No. 7, pp.1 6 (2009) [6],,,,,, TETDM,, Vol. 28, No. 1, pp (2013) [7],, 3, SIG-AM-03-07, pp (2013) [8] R TETDM, 27, 3B3-NFC-01b-2 (2013)
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24 Region-Based Image Retrieval using Fuzzy Clustering 1130323 2013 3 9 Visual-key Image Retrieval(VKIR) k-means Fuzzy C-means 2 200 2 2 20 VKIR 5 18% 54% 7 30 Fuzzy C-means i Abstract Region-Based Image
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