情報処理学会論文誌 コンシューマ デバイス & システム Vol.6 No (May 2016) 図 1 DISAANA のスクリーンショット 2015/9/2 時点 質問応答モードにおける質問 東 エリア検索モードにおける質 京で何が発生していますか の結果を PC で表示 左
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1 & Vol.6 No (May 2016) SNS DISAANA 1,a) 1, 1,b) 1,c) 1, 2,d) 1,e) 1,f) 1,g) 1,h) 1,i) , Twitter SNS DISAANA PC Web DISAANA 192 F 7 DISAANA SNS Improving Question Answering of Disaster-information Analyzer (DISAANA) Using Modality Analysis Junta Mizuno 1,a) Jun Goto 1, 1,b) Kiyonori Ohtake 1,c) Takuya Kawada 1, 2,d) Kentaro Torisawa 1,e) Kloetzer Julien 1,f) Masahiro Tanaka 1,g) Chikara Hashimoto 1,h) Akitoshi Okumura 1,i) Received: October 1, 2015, Accepted: February 23, 2016 Abstract: We developed a web service called DISAANA that can be used by anyone from such terminals as smartphones and computers to efficiently retrieve information about natural disasters from the massive amount of posts about them. In this paper, first, we describe the construction of the previous system and its problems. Second, we describe how DISAANA solves them by focusing on modality analysis, which prevents the extraction of incorrect answer candidates. We evaluated the question answering performance of the tweets posted during the 2011 Great East Japan Earthquake using 192 manually constructed questions and their answers. DISAANA achieved a 63.0 F-measure, which outperformed the previous system s 56.0 score. We discuss further directions based on error analysis and report the results of demonstration experiments of DISAANA for a local government context. Keywords: disaster information, question answering, information retrieval, social network services 1 NICT, Kyoto , Japan 1 NHK Presently with NHK STRL 2 NEC Presently with NEC Knowledge Discovery Research Laboratories a) junta-m@nict.go.jp b) goto.j-fw@nhk.or.jp c) kiyonori.ohtake@nict.go.jp d) t-kawada@cw.jp.nec.com e) torisawa@nict.go.jp f) julien@nict.go.jp g) mtnk@nict.go.jp h) ch@nict.go.jp i) okumura@nict.go.jp c 2016 Information Processing Society of Japan 106
2 情報処理学会論文誌 コンシューマ デバイス & システム Vol.6 No (May 2016) 図 1 DISAANA のスクリーンショット 2015/9/2 時点 質問応答モードにおける質問 東 エリア検索モードにおける質 京で何が発生していますか の結果を PC で表示 左図 問 北海道 の結果をモバイル端末で表示 右図 した様子 Fig. 1 Example screenshots of DISAANA (revision of Sep. 2, 2015): left side shows answer candidates for question What s the situation now in Tokyo? on personal computers, and right side shows answer candidates for Hokkaido by area search mode on smartphones. 1. はじめに ものを考えるのは困難であるとの指摘が ある地方自治体 よりあった そこで DISAANA では 市町村などのエリ 東日本大震災では Twitter に膨大な量の災害関連情報が アを指定するとそこで起きているトラブルや問題を自動的 投稿された 米 Twitter 社によると 1 秒あたりのツイート に検出する機能も提供する DISAANA では 前者を質問 数が 5,000 件を超えることが 5 回あり 日本からのツイート 応答モード 後者をエリア検索モードと呼ぶ 数は地震発生後に 500%増加した*1 震災に限らず 災害時 質問応答モードでは 自然文による質問を入力すると に Twitter に投稿される災害関連情報は 即時性が高く重 その回答候補を一覧することができる たとえば X 市 要な情報が含まれる一方で 投稿数が膨大であるため 一般 で何が不足していますか という質問を入力すると X 的なキーワード検索によって必要な情報を効率良く入手す 市で毛布が不足している や X 市の病院で透析用チュー ることは困難である そこで 我々は災害関連情報をリア ブが足りない といったツイートから 質問の答えとなる ルタイムに効率的に検索することができるシステムとして 毛布 透析用チューブ などが得られる つまり 不足 対災害 SNS 情報分析システム DISAANA を開発した 本 する と 足りない といった表現の違いを吸収したうえ システムは スマートフォンおよび PC 経由で誰でも利用 で ピンポイントに質問の回答候補を網羅的に出力する 可能な Web アプリケーションとして 質問応答モードの PC での動作例を 図 1 左側に示す こ で試験公開されている の例は 東京で何が発生していますか という質問を入 DISAANA は 災害時に発信される膨大な情報から必要 力して検索した結果であり 火災 地震 落雷がある とする情報を効率的に発見し 災害状況などを俯瞰的に把 といった災害情報や 運休がある 事故 といったトラ 握できるよう質問応答技術 すなわち 自然言語で表され ブルが検索されている それぞれの回答候補をクリックす た文による質問に対して 回答となる名詞や文を出力する ると その抽出元となったツイートを閲覧することができ 技術を用いた情報アクセス手段を提供する 一方で 我々 る*2 DISAANA が検索対象とするツイートは 日本語で が事前に調査したところでは こうした質問応答手段が 書かれた全ツイートの 10%*3 のうち 当日を含む直近の 4 あったとしても災害時の逼迫した状況の中では 質問その *2 *1 c 2016 Information Processing Society of Japan *3 表示する段階でユーザによってすでに削除されたツイートは表示 されない menu.html 107
3 & Vol.6 No (May 2016) DISAANA [1]Y Y Y Z 1 PC [2] 1 X X DISAANA 2 2 Fig. 2 Example screenshot which shows information contradicting with answer candidate on smartphones. 1 *4 X DISAANA 5 6 DISAANA *4 c 2016 Information Processing Society of Japan 108
4 & Vol.6 No (May 2016) 7 2. IBM Watson [3] Watson Wikipedia Watson DISAANA [4] DB Factoid DISAANA [5] [6] DISAANA DISAANA 3 Fig. 3 Examples of event and predicate for modality analysis. 3 [7] Conditional Random Fields CRF [8] Saurí [9] Factuality Profiler Saurí 2 positive negative unknown 3 certain probable possible unknown 4 2 Saurí TimeBank [10] FactBank [11] FactBank source c 2016 Information Processing Society of Japan 109
![4 Prototype system architecture. 3.1 *5 Juman [12] MeCab [13] J. DepP [14] 2 1 *5 6 Fig.](/docs-images/93/113263792/images/5-2.jpg "6 Example of question answering process for question where is it snowing?")
5 & Vol.6 No (May 2016) Fig. 5 5 X Dependency structure of it is snowing heavily in X city. 4 Fig. 4 Prototype system architecture. 3.1 *5 Juman [12] MeCab [13] J. DepP [14] 2 1 *5 6 Fig. 6 Example of question answering process for question where is it snowing?. X 5 A B A X B A A X B B 2 * A B A B A *6 A B A A B c 2016 Information Processing Society of Japan 110
![& Vol.6 No.1 106 120 (May 2016) [15] A B A B A 1 1 A A A A A A A [16] A A 3.3 5 3 1 2 A A A B B A 3 1 5,000 4 4.](/docs-images/93/113263792/images/6-0.jpg "DISAANA DISAANA 7 (1) 4.1 (2) 4.2 (3) 7 DISAANA Fig. 7 DISAANA architecture.")
6 & Vol.6 No (May 2016) [15] A B A B A 1 1 A A A A A A A [16] A A A A A B B A 3 1 5, DISAANA DISAANA 7 (1) 4.1 (2) 4.2 (3) 7 DISAANA Fig. 7 DISAANA architecture. c 2016 Information Processing Society of Japan 111
7 & Vol.6 No (May 2016) RaSC [17] (4) DISAANA X 5 1 X [7] ID N N N N 1 4 N word2vec [18] N N N k-means 8 Fig. 8 An example of modality analysis. SVM [19] 1 LIBSVM [20] N ID ID SVM 1 N word2vec k-means ID Kazama [21] ID 7 2,000 word2vec k-means 1 3 c 2016 Information Processing Society of Japan 112
8 & Vol.6 No (May 2016) [22] 4.2 X 1 Table 1 List of nouns indicating disasters and damage for pattern extraction. A B A X B *7 1,000 X X *8 1 A B A B [16] X Y X Y *7 li-info/li-outline.html#a-3 *8 c 2016 Information Processing Society of Japan 113
9 & Vol.6 No (May 2016) 7 8 7/ ,996 Wikipedia *9 Wikipedia 3 * X 2 7 RT * 10 URL DISAANA DISAANA 4.5 [15] [23], [24], [25] X X Y Y DISAANA *10 c 2016 Information Processing Society of Japan 114
10 & Vol.6 No (May 2016) 2 5 Table 2 Results of modality analysis by 5-fold cross validation. F (39053/53117) (39053/44672) (4137/5999) (4137/8546) (9848/11827) (9848/13480) (10785/17651) (10785/19239) (9710/12278) (9710/14935) , N N Wikipedia 2015/1/ % 2015/2/ /2/ GB 4.5 GB 4.3 GB word2vec word2phrase word2vec k-means word2vec 8 (50, 100, 150, 200, 250, 300, 350, 500) 6 (100, 500, 1000, 2000, 5000, 10000) 5 Wikipedia ,824 4, DISAANA 8, ,400 5,400 3,100 DISAANA 10 * 11 [26] 300 5, , DISAANA 1, % *11 DISAANA 1 c 2016 Information Processing Society of Japan 115
11 & Vol.6 No (May 2016) Table 3 3 Results of question answering using tweets posted during Great East Japan Earthquake. F (152/250) (9,099/17,524) DISAANA (142/250) (12,382/17,524) F X % A A A % A A X X % A % % 6. DISAANA DISAANA c 2016 Information Processing Society of Japan 116
12 & Vol.6 No (May 2016) 9 DISAANA Fig. 9 Outline of DISAANA demonstration. DISAANA SNS DISAANA 9 SNS Twitter Twitter 73.5 DISAANA DISAANA SNS DISAANA ,400 1,760 DISAANA 18 NICT PC DISAANA PC 2 2 DISAANA DISAANA c 2016 Information Processing Society of Japan 117
13 & Vol.6 No (May 2016) 4 DISAANA Table 4 Questionnaire results: Is DISAANA useful for disaster situations? DISAANA % DISAANA GPS DISAANA 7. Twitter DISAANA Web F 7 [1] Varga, I., Sano, M., Torisawa, K., Hashimoto, C., Ohtake, K., Kawai, T., Oh, J.-H. and De Saeger, S.: Aid is Out There: Looking for Help from Tweets during a Large Scale Disaster, Proc. 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp (2013). [2] Stijn, D.S. Julien, K. Vol.20, No.3, pp (2013). [3] Ferrucci, D., Brown, E., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A.A., Lally, A., Murdock, J.W., Nyberg, E., Prager, J., Schlaefer, N. and Welty, C.: Building Watson: An Overview of the DeepQA Project, AI Magazine, Vol.31, No.3, pp (2010). [4] IPSJ SIG Technical Report, Vol.2012-SLP-93, No.4, pp.1 6 (2012). [5] Japio year book, pp (2009). [6] D Vol.93, No.6, pp (2010). [7] 16 pp (2010). [8] Sutton, C., McCallum, A. and Rohanimanesh, K.: Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data, The Journal of Machine Learning Research, Vol.8, pp (2007). [9] Saurí, R. and Pustejovsky, J.: Determining Modality and Factuality for Textual Entailment, Proc. 1st IEEE International Conference on Semantic Computing, pp (2007). c 2016 Information Processing Society of Japan 118
![& Vol.6 No.1 106 120 (May 2016) [10] Pustejovsky, J., Verhagen, M., Saurí, R., Littman, J., Gaizauskas, R., Katz, G., Mani, I., Knippen, R. and Setzer, A.: TimeBank 1.2 (2006). [11] Saurí, R.](/docs-images/93/113263792/images/14-0.jpg "and Pustejovsky, J.: FactBank: a corpus annotated with event factuality, Language Resources and Evaluation, Vol.43, No.3, pp.227 268 (2009). [12] Kurohashi, S., Nakamura, T., Matsumoto, Y.")
![and Nagao, M.: Improvements of Japanese morphological analyzer JUMAN, Proc. International Workshop on Sharable Natural Language, pp.22 28 (1994). [13] Kudo, T., Yamamoto, K. and Matsumoto, Y.](/docs-images/93/113263792/images/14-1.jpg ": Applying Conditional Random Fields to Japanese Morphological Analysis, Proc. 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP 2004 ), pp.230 237 (2004). [14] Yoshinaga, N.")
14 & Vol.6 No (May 2016) [10] Pustejovsky, J., Verhagen, M., Saurí, R., Littman, J., Gaizauskas, R., Katz, G., Mani, I., Knippen, R. and Setzer, A.: TimeBank 1.2 (2006). [11] Saurí, R. and Pustejovsky, J.: FactBank: a corpus annotated with event factuality, Language Resources and Evaluation, Vol.43, No.3, pp (2009). [12] Kurohashi, S., Nakamura, T., Matsumoto, Y. and Nagao, M.: Improvements of Japanese morphological analyzer JUMAN, Proc. International Workshop on Sharable Natural Language, pp (1994). [13] Kudo, T., Yamamoto, K. and Matsumoto, Y.: Applying Conditional Random Fields to Japanese Morphological Analysis, Proc Conference on Empirical Methods in Natural Language Processing (EMNLP 2004 ), pp (2004). [14] Yoshinaga, N. and Kitsuregawa, M.: Polynomial to Linear: Efficient Classification with Conjunctive Features, Proc Conference on Empirical Methods in Natural Language Processing (EMNLP 2009 ), pp (2009). [15] Kloetzer, J. Saeger, S.D. Supervised Recognition of Entailment Between Patterns, 18 pp (2012). [16] Hashimoto, C., Torisawa, K., De Saeger, S., Oh, J.-H. and Kazama, J.: Excitatory or Inhibitory: A New Semantic Orientation Extracts Contradiction and Causality from the Web, Proc Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP- CoNLL 2012 ), pp (2012). [17] RaSC 20 pp (2014). [18] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S. and Dean, J.: Distributed representations of words and phrases and their compositionality, Advances in neural information processing systems, pp (2013). [19] Vapnik, V.: The nature of statistical learning theory, Springer Science & Business Media (2000). [20] Chang, C.-C. and Lin, C.-J.: LIBSVM: A library for support vector machines, ACM Trans. Intelligent Systems and Technology, Vol.2, pp.27:1 27:27 (2011). [21] Kazama, J. and Torisawa, K.: Inducing Gazetteers for Named Entity Recognition by Large-Scale Clustering of Dependency Relations, Proc. ACL-08: HLT,pp (2008). [22] Oh, J.-H., Torisawa, K., Hashimoto, C., Kawada, T., De Saeger, S., Kazama, J. and Wang, Y.: Why question answering using sentiment analysis and word classes, Proc Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2012 ), pp (2012). [23] Kloetzer, J., Torisawa, K., Hashimoto, C. and Oh, J.-H.: Large-Scale Acquisition of Entailment Pattern Pairs by Exploiting Transitivity, Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP 2015 ), pp (2015). [24] Sano, M., Torisawa, K., Kloetzer, J., Hashimoto, C., Varga, I. and Oh, J.-H.: Million-scale Derivation of Semantic Relations from a Manually Constructed Predicate Taxonomy, Proc. COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp (2014). [25] Kloetzer, J. 21 pp (2015). [26] 19 pp (2013) NHK 2001 ATR c 2016 Information Processing Society of Japan 119
15 & Vol.6 No (May 2016) NEC JST AAMT NEC c 2016 Information Processing Society of Japan 120
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