2 Tweet2Vec Twitter Vosoughi Tweet2Vec[11] WordNet 2.2 Ver.2 Ver Twitter 8 38,576 Ver.2 Twitter 2. Twitter 2.1 [7], [9] n 1 n 1 X=(x 1,, x
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1 Ver.2 Twitter 1,a) Ver.2 2 Ver Twitter 8 38,576 ver.2 Twitter word2vectwitter 1. Mikolov word2vec [1], [2], [3]Le Mikolov [4] Association for Computer Linguistics 2013 Twitter SemEval [5], [6] SemEval Twitter Takayama-cho, Ikoma, Nara , Japan 2 IoT Nakase, Mihama-ku, Chiba, Chiba , Japan a) keshi.ikuo.ka9@is.naist.jp Twitter Twitter [7], [8], [9] F [9] Twitter Unicode Twitter 2 Tweet2Vec [10], [11] Vosoughi Tweet2Vec[11] WordNet[12] 300 LSTM-CNN SemEval2015[5] 9,520 2,380 F 1.9 SemEval F Dhingra Tweet2Vec[10] c 2017 Information Processing Society of Japan 1
2 2 Tweet2Vec Twitter Vosoughi Tweet2Vec[11] WordNet 2.2 Ver.2 Ver Twitter 8 38,576 Ver.2 Twitter 2. Twitter 2.1 [7], [9] n 1 n 1 X=(x 1,, x n ) { } (1, 0, 0, 1, 1, 0) *1 *2 WWW Twitter Twitter *1 CD, TBS, *2 CD- 94, 95,, 1994, c 2017 Information Processing Society of Japan 2
3 3 [8], [9] Twitter PV-DM PV-DBOW Skip-gram PV-DBOW PV-DM, PV-DBOW SVM 1 F PV-DBOW 2.3 [13] ツイート 基本単語 展開された 特徴単語 4 ( 製品 A) 真偽はともかくとして (A 社 ) 製端末で 4. 3 インチ FullHD 画面は非常に魅力的 真偽 教育 育児 社会問題 複雑 知識 言論 発話 思想 哲学 数学 INPUT Paragraph id w(t-3) w(t-2) w(t-1) 2 製 衣類 日用品 装飾品 道具 機械 機器 自動車 船舶 航空機 会社 職業 製造業 1 Concatenate/ Sum / Average PV-DM 端末 機械 機器 通信 電気工学 電子工学 コンピュータ ハード システム OA 音響 通信技術 Classifier インチ 衣類 道具 数量 数学 製造 工作 Paragraph id w(t) w(t) INPUT 画面 通信 マスメディア 色彩 平面 映像 画像 電子工学 コンピュータ 通信技術 PV-DBOW Skip-gram 非常 様子 様態 秩序 順序 勢力 程度 特殊 希有 困難 Classifier 2 w(t-4) w(t-3) w(t-2) w(t-1) 魅力 感覚 感情 喜楽 勢力 程度 価値 質 優良 肯定的 美麗 性質 明るさ Posi- (2.0) (2.7) (2.2) (4.4) tive (2.2) (2.4) (2.1) (3.3) (2.6) (2.3) (3.1) (3.6) (2.4) (5.0) (2.5) (2.4) (3.2) (2.1) (3.9) (4.4) Nega- (1.6) (2.9) (6.3) (2.2) tive (2.0) (3.7) (3.1) (14) X 1 :0 X 2 :0 x V :0 (2.0) (3.0) (2.6) (1.6) 3 (2.4) (3.0) (4.8) (2.3) (3.0) (2.3) (4.0) Skip-gram [14] 1 One-hot Faruqui Retrofitting *3 [14] Skip-gramPV-DBOW *3 c 2017 Information Processing Society of Japan 3
4 製品 B すげぇ 勢 程度, 強, 間, 価値 質, 様 様態, 施設 設備, 教育 育児, 肯定的, 優良, 製品 B は神 様 様態, 思想 哲学, 間, 関係 関連, 死, 実存, 勢 程度, 棲 物, 国家, 強, あーやっぱり製品 B いい w なんか深み? がある w 響, 宣伝広告, 様 様態, 映像 画像, 感情, 価値 質, 楽, 勢 程度, 感覚, 劣悪, 製品 B で 楽きいたら 質めちゃくちゃよくてビックリした w 様 様態, 楽, 施設 設備, 動作, 倫理 道徳, サービス業, 感情, 数量, 価値 質, 化, 製品 B って充電終わっても led 点灯したまんまなんだ 明るさ, 機械 機器, 発熱 発光, 様 様態, コンピュータ, 活動, 実質 本質, 新しさ, 建造物, 4 5 5,188 4,460 3,217 2,689 2,645 2, PV-DBOW 5 B B 2 led 3. Ver Ver.2 Ver.2 Ver Ver.2 20, , Microsoft Translator API *4 14,000 TOEIC A TOEIC ,551 7, ,912 Ver2. 6 *4 c 2017 Information Processing Society of Japan 4
5 単語意味ベクトル辞書 (2 万 330 語 ) ニューラル機械翻訳サービス利 クラウドソーシングで TOEIC900 点以上 本, 本の新聞を読むネイティブ募集 (65 名応募, 内 45 名が無償のトライアルに参加.3 名採択 ) クラウドワーカー A( 個 ) 13,551 英単語 語句 ( 内 7,692 翻訳 ) クラウドワーカー B( 個 ) 14,119 英単語 語句 ( 内 11,407 翻訳 ) クラウドワーカー C( チーム ) 13,628 英単語 語句 ( 内 9,144 語翻訳 ) 英語版意味ベクトル辞書 (21,912 語 ) 5 演奏会娯楽 趣味共同多数 多量 化芸術 楽 響 化学微 物植物 間の 体内臓器官 物の 体健康 美容医学 薬学 遠 勢 程度 規模広 未来思想 哲学計画短銃殺 機械 機器軍事 防衛戦争 紛争短さ軍事技術建つ住居建造物施設 設備 体形状 さ 建築建設業出来誕 活動社会問題災害変化価値 質因果肯定的新しさ容易強 動作 建て住居建造物会社 職業施設 設備国家経済 融秩序 順序勢 程度 雪氷気象 気候凝固 凍結寒冷 厳し活動財政エネルギー問題国際関係極地感覚否定的複雑困難沖積陸地環境変化地理地学平然感覚感情勢 程度 般 平凡単純容易強 理性的個 静的軽さ auctions economy behavior vast commerce customer audicious emotion strong property behavior substantial thickness idea audience hobby sport enjoyment majority general individual art image music customer audience_seats sport structure facility space art image music customer audio human language sound hobby machine music electronics audio_source machine computer software sound audio_training japan knowledge discussion book literature language audiovisual human_body visceral_organ health sense medicine audiovisual_senses human_body visceral_organ health sense medicine 6 Ver Twitter Twitter B B 1 45% A [8], [9] 2 [9] 7 AB & Twitter 8 c 2017 Information Processing Society of Japan 5
6 7 & A 130,650 2,906 5,188 16, ,158 92,900 B 482,036 5,655 9,531 51, ,884 86,573 C 1,155,034 3,543 6,176 45, ,844 84,539 A 11, , ,371 12,358 B 307, ,089 20, ,092 70, ,097 3,887 3,484 30, ,514 73,302 A 187, ,421 40, ,358 72,548 B 169,532 1, , ,891 71,035 2,718,753 19,933 31, ,820 2, , ,099 8 Twitter 2 10,100 15,618 25, , , ,993 2,525 3,904 6,429 34,272 45,046 85,747 2,525 3,904 6,429 34,272 45,046 85,747 15,150 23,426 38, , , ,487 2,204, ,213 79,640,916 2,910 12,937 単語意味ベクトル辞書のシードベクトルを いたコーパスのバルク学習 ランダム設定の初期ベクトルを いたコーパスのバルク学習 パラグラフベクトルによる訓練セットと開発セットの特徴抽出 %(0.3%) 88.2% (0.1%) 88.7%(0.4%) 88.6% (0.2%) 訓練セットの交差検証による SVM の分類器構築 開発セットを対象に特徴抽出のパラメータ ( 学習回数 ) 調整 & 2 38,576 SemEval 3 3 F テストセットによる評価 7 5. Ver.2 PV- DBOW Twitter Twitter c 2017 Information Processing Society of Japan 6
7 89.2% 88.2%5 B [,,,,,, ] A A... [,,,,,,, ] [,,,,, ] B ok [,,,,,, ] 6. Ver.2 Twitter Ver.2 Twitter 2 88% Ver.1 20 Twitter 1 10% Ver.2 Twitter 1 Ver.2 NAIST [1] T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space, Proc. of Workshop at ICLR, [2] T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, Distributed representations of words and phrases and their compositionality, Proc. of NIPS, pp , [3] T. Mikolov, W. Yih, and G. Zweig, Linguistic regularities in continuous space word representations, Proc. of NAACL HLT, pp , [4] Q. Le, T. Mikolov, Distributed representations of sentences and documents, Proc. of ICML, pp , [5] S. Rosenthal, P. Nakov, S. Kiritchenko, S. M Mohammad, A. Ritter, and V. Stoyanov, SemEval-2015 Task 10: Sentiment analysis in Twitter, Proc. of SemEval- 2015, pp , [6] P. Nakov, A. Ritter, S. Rosenthal, F. Sebastiani, and V. Stoyanov, SemEval-2016 task 4: Sentiment analysis in Twitter, Proc. of SemEval-2016, pp.1-18, [7] Dvol.J79-D-IIno.4 pp [8] Twitter 8 (A1-3)2016. [9] Twitter D vol.j100-dno.4pp [10] B. Dhingra, Z. Zhou, D. Fitzpatrick, M. Muehl, and W. Cohen, Tweet2Vec: Character-Based Distributed Representations for Social Media, Proc. of ACL, vol.2, pp , [11] V. Soroush, V. Prashanth, and R. Deb, Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decode, Proc. of SIGIR, pp , [12] C. Fellbaum, WordNet, Wiley Online Library, [13] vol.46, no.2, pp , [14] M. Faruqui, J. Dodge, S. Jauhar, C. Dyer, E. Hovy, and N. Smith, Retrofitting Word Vectors to Semantic Lexicons, Proc. of NAACL, pp , c 2017 Information Processing Society of Japan 7
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