IPSJ SIG Technical Report Vol.2012-DCC-1 No /5/18 1,a) 2,b) 3,c) 4,d) ( ) Discussion Mining with Music Theory Being Applied to Analysis of Meet
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1 1,a) 2,b) 3,c) 4,d) () Discussion Mining with Music Theory Being Applied to Analysis of Meeting Record Keiji Hirata 1,a) Katashi Nagao 2,b) Satoshi Tojo 3,c) Masatoshi Hamanaka 4,d) Abstract: Discussion mining is a technology by which activities in meetings are recorded into texts, video, and meta data, from which resusable data is extracted. Music theory is a technology that parses the chronological sequence of musical events, melody, metrical, and harmony. The aim of the research is parsing the discussion of a meeting in the way of music theory, considering a meeting as a sequence of utterances. The paper presents the results of studying the idea that music theory is applied to the analysis of meeting records. 1. (DM) DM 1 Future University Hakodate 2 Nagoya University 3 Japan Advancec Institute of Science and Technology 4, JST University of Tsukuba, PRESTO JST a) hirata@fun.ac.jp b) nagao@nuie.nagoya-u.ac.jp c) tojo@jaist.ac.jp d) hamanaka@iit.tsukuba.ac.jp DM () 1 c 2012 Information Processing Society of Japan 1
2 情報処理学会研究報告 発表開始 議論セグメント 質疑応答 終了 映像 音声コンテンツ スライドコンテンツ 発表セグメント 発言セグメント 導入発言 継続発言 継続発言 DM木 図 1 記録されるコンテンツと生成される DM 木 図 2 会議内容閲覧ページ ける単語 (終端記号) に対応すると考えると 単語の品詞に 相当する概念が存在せず 構文という概念も存在しない 楽曲全体の意味として 楽曲を階層的に分節する構造と同 一視する立場もある 本研究の目的は 会議における議論を発言の時系列とみ なし 音楽理論の手法を用いて議論を 構文解析 し重要 な発言を同定することである 重要な発言を自動的に同定 できればより有用な DM が実現できよう 本稿では 議論 の記録から重要な発言を取りだす処理に関して DM の会 議内容閲覧ページに表示される各発言を読んで判断する方 法 従来の DM が採用している活性拡散による方法 音楽 理論を応用した方法を比較し 音楽理論を会議記録の分析 に応用することの有効性を検討する 2. ディスカッションマイニングシステム 2.1 記録される会議コンテンツ 図 3 会議内容閲覧ページに表示されたある話題に関する一連の発 言 (例 1) 取り出すため 発話ごと 文章ごと 発言ごとに人手でタ グやアノテーションを付与するのはコストの観点から現実 現在構築中のディスカッションマイニング (DM) システ 的ではないだろう しかし 自動タグ アノテーション付 ムが支援している会議スタイルは 発表者がスライドを表 与では十分な精度を達成するのは難しい そこで DM シス 示しながらその説明を行い 発表の途中あるいは終了のタ テムでは 上記のように 会議中の参加者が負担にならな イミングで会議参加者との質疑応答が始まるというもので い程度のコストでメタ情報を付与する仕組みを採用した ある DM システムは 発表者によるスライド発表と続く DM システムは 取得した各種メタデータとともに会議 参加者との質疑応答を分節して記録する (図 1) 新しい話題に関する質問やコメントを発言する参加者は 中の発言を表示する (図 3) 図 3 のサイズが小さく読みに くいので各発言の要約を発言順に表 1 に示す 各発言内容 専用端末を用いて自分の発言に導入タグを付与する 直前 の要約の左側は 上左が発言番号 (例: 77 ) 上右が発言 の発言に密接に関連した発言を行いたい場合は継続タグを 者 (例: M) 下左が賛成ボタンが押された回数 (例: 1) 下 付与する さらに スライドに対するポインタ指示場所 右が発言に要した時間 (例: 2:49 (2 分 49 秒の意)) である 時刻の記録 発表中や質疑応答中の発言に対する賛成/反 DM システムは各ユーザに賛成ボタンという専用入力装置 対ボタン情報 重要箇所のマーキング情報も記録し 後処 を提供しており ユーザがある発言に賛同したり重要だと 理でそれらメタデータを基に話題の流れの認識と重要発言 感じた場合などは賛成ボタンを押して高い評価を与えたこ の同定を行う 会議内容閲覧ページでは 日付や参加者情 とを明示的に DM システムに通知する (4.3 節) 報から議事内容の検索 現在進行中の議論と類似した過去 スライド発表者や会議参加者は各自ポインタを用いてス の議論の閲覧 議論の様子の効果的な可視化 (図 2) などが ライド上の文字や図形を指示することができ どこをいつ できる 指示したかの情報が記録される ポインタによるスライド 議論を話題単位に正しく分節し話題セグメントととして c 2012 Information Processing Society of Japan 上の指示領域として 下線選択 (文字列) と矩形選択 (図形) 2
3 , M 1, 2:49 78, T 0, 1:27 79, O 1, 1:53 80, N 0, 3:46 81, N 3, 3:38 DM 100% 36, W 0, 0:43 37, Y 0, 1:13 38, M 0, 1:11 39, T 0, 0:57 40, K 0, 0:50 41, O 6, 2: , W 0, 0:33 43, Y 0, 0: DM 2 (4.3 ) 2.2 DM DM DM DM 1 DM DM 4 1 DM M 77 T O DM DM ( 2 5) , N 0, 2:19 45, I 0, 0:48 46, Y 0, 0: DM Fred Lerdahl Ray Jackendoff 1983 Generative Theory of Tonal Music (GTTM) 1 [4] GTTM c 2012 Information Processing Society of Japan 3
4 q eq 6 7 GTTM (TS ) ( 6 ) TS *1 2 TS 6 TS C4, F4, G4 F4 G4 P P C4 Q() P F4 G4 Q G4 C4 C4 *2 GTTM TS (G) (S) G S *3 *4 4. ( TS ) TS ( TS ) 4.1 *1 GTTM *2 [2] *3 *4 ( ) [7] N : Q&A n A c 2012 Information Processing Society of Japan 4
5 a(1, 1) a(1, n) A = (1) a(n, 1) a(n, n) a(i, j) i j 1 0 A W W A n n C ( 7 ) c(1) C =. (2) c(n) c(i) i X 0 = C, X t+1 = W X t + C t (= 0, 1, 2,...) x(t, 1) X t =. (3) x(t, n) x(t, i) t i t t X = W X + C X = (E W ) 1 C ( E ) 4.3 DM DM / [1] i j i j GUI (/ / ) > 77 > 79 > 78 > > 36 > 39 > 44 > 42 > 37 = 38 = 40 > 43 > 45 > TS TS TS TS DM 3 DM GTTM GTTM c 2012 Information Processing Society of Japan 5
6 TS DM 1 1 TS GTTM TS GTTM *5 GTTM GTTM *6 DM TS TS DM 4 DM DM TS TS 1 TS TS ( 9 10) TS ( ) 9 TS 81 > 77 > > 80 > 77 > 79 *5 1 2 * TS 10 TS 41 > 36 > 39 > 40 > 38 > > 43 > > 44 > 45 > 46 TS ( 1 81 ) TS 1 TS TS (4.3 ) TS DM 2 TS > = TS 2 TS 4.1 ground truth TS TS > = 2 TS > 5. GTTM TS c 2012 Information Processing Society of Japan 6
7 (1) GTTM (2) TS DM 1 Q&A ( ) Q&A () Q&A Q&A TS TS (join, meet ) [6] TS join, meet A Generative Theory of Tonal Music, Journal of New Music Research, vol.35, no.4, pp (2007). [3] Hamanaka, M., Hirata, K. and Tojo, S.: Melody Morphing Method Based on GTTM, Proc. of ICMC 2008, ICMA, pp [4] Lerdahl, F. and Jackendoff, R.: A Generative Theory of Tonal Music, The MIT Press (1983). [5] Nagao, K., Kaji, K., Yamamoto, D. and Tomobe, H.: Discussion Mining: Annotation-Based Knowledge Discovery from Real World Activities, Proc. of Pacific-Rim Conference on Multimedia (PCM 2004), pp (2004). [6] Tojo, S. and Hirata, K.: Structural similarity based on time-span tree, Proc. of 9th International Symposium on Computer Music Modeling and Retrieval (CMMR) 2012, to appear. [7] :, doctor thesis11.xml (2011). [8] :, ( (C) ) [1] Collins, A.M. and Loftus, E.F.: A Spreading Activation Theory of Semantic Processing. Psychological Review, vol.82, no.6, pp (1975). [2] Hamanaka, M., Hirata, K. and Tojo, S.: Implementing c 2012 Information Processing Society of Japan 7
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Directability 1957 1. 1950 Meyer [1] 1970 Higgins [2] 1980 Deutsch [3] 1980 [4] Lerdahl Jackendoff Generative Theory of Tonal Music (GTTM)[5] Narmour Impication - Realization Model (IRM) [6] 1950 1957
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The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). The material has been made available on the website
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