Outline ACL 2017 ACL ACL 2017 Chairs/Presidents

Size: px
Start display at page:

Download "Outline ACL 2017 ACL ACL 2017 Chairs/Presidents"

Transcription

1 ACL 2017, 2017/9/7

2 Outline ACL 2017 ACL ACL 2017 Chairs/Presidents

3 ACL ACL he annual meeting of the Association for Computational Linguistics (Computational Linguistics) (Natural Language Processing) / Beijing (2015) Berlin (2016) Vancouver (2017) Melbourne (2018) Florence (2019)

4 ACL 2017 (1) 7/30~8/4 7/30 7/31~8/2 8/3~8/ ACL

5 ACL 2017 (2) Vancouver Westin Bayshore Hotel 302 long: 195, short: % TACL 21 21

6 ACL

7 Chairs/Presidents (1) Challenges for ACL Computational Linguistic (CL) is booming! Equity and Diversity Publishing and Reviewing Good Science 4 3

8 Chairs/Presidents (2) CL is booming! 2017 Joakim Nivre presidential-address-acl-2017-challenges-for-acl 10 2

9 Chairs/Presidents (3) Equity and Diversity ACL Area Chair 2017 knmnyn last-call-for-area-chairs-a-call-for-diversity/ Area Chair

10 Chairs/Presidents (4) WiNLP 第 1 ACL 2017 big interest group 2017 WiNLP

11 Chairs/Presidents (5) Publishing and Reviewing / arxiv preprint( )

12 Chairs/Presidents (6) ACL % preprint 27% preprint 88% 87% preprint ACL

13 Chairs/Presidents (7) preprint preprint preprint

14 2 Squashing Computational Linguistics : Noah A. Smith Translating from Multiple Modalities to Text and Back : Mirella Lapata 2 1 2

15 Translating from Multiple Modalities to Text and Back (1) Mirella Lapata (University of Edinburgh) Encoder-Decoder ( ) Scream ( ) 2017 Mirella Lapata aclanthology/mirella-lapata translating-from-multiplemodalities-to-text-and-back

16 Translating from Multiple Modalities to Text and Back (2) The Simplification Task Language to Code Sequence-to-Tree Movie Summarization

17 2010 (2009 )

18 Social Event: Vancouver Aquarium ACL

19 2 1

20 Neural Model (1) neural, lstm, recursive, rnn, recurrent, cnn, convolution, dnn, deep, embedding, distributed representation EMNLP 2015 ( 2015) ( ) 70/312 (22.4%) NAACL-HLT 2016 ( 2016) 71/182 (39.0%) ACL 2016 EMNLP 2016 ( 2016) ACL /328 (29.6%) 92/264 (34.8%) 113/302 (37.4%)

21 Neural Model (2) ( 2015; 2016; 2016) 挙がったアプローチ embedding, attention, encoder-decoder variational, reinforce pdfgrep

22 Neural Model (3) embedding ACL 2017 ACL 2016 NAACL 2016 neural embedding 38 (23.2%) 45 (37.5%) 16 (22.9%) attention 59 (36.0%) 35 (29.2%) %) encoder-decoder 19 (11.6%) 10 (8.3%) 4 (5.7%) variational 4 (2.4%) 0 (0.0%) 0 (0.0%) reinforce 6 (3.7%) 2 (1.7%) 1 (1.4%) neural

23 Neural Model ( ) 2 Variational Auto-Encoder Approaches Morphological Reinflection Reinforcement Learning Approaches Semantic Parsing

24 Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction Chunting Zhou, Graham Neubig Variational Encoder-Decoder Morphological Reinflection

25 Coarse-to-Fine Question Answering for Long Documents Eunsol Choi, Daniel Hewlett, Jakob Uszkoreit, Illia Polosukhin, Alexandre Lacoste, Jonathan Berant QA RNN 3.5~6.7

26 Social Media Social Media social media, social network, twitter, tweet

27 EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks Muhammad Abdul-Mageed, Lyle Ungar Distant Supervision GRNNs accuracy=87.58% GRNNs RNN Gated Recurrent Unit (GRU) 56.84~62.10% %

28 Demographic Inference on Twitter using Recursive Neural Networks 著者 Sunghwan Mac Kim, Qiongkai Xu, Lizhen Qu, Stephen Wan, Cecile Paris 概要 Twitter Recursive Neural Networks Bag-of-Words

29 Overcoming Language Variation in Sentiment Analysis with Social Attention Yi Yang, Jacob Eisenstein TACL Sentiment Analysis k Author Embedding attention Twitter Sentiment Analysis

30 preprint Neural Model Neural Social Media

31 NAACL-HLT 2016 ~ ~ EMNLP EMNLP 2015 (2). 2. Muhammad Abdul-Mageed, Lyle Ungar EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks. In Proc. of ACL Eunsol Choi, Daniel Hewlett, Jakob Uszkoreit, Illia Polosukhin, Alexandre Lacoste, Jonathan Berant Coarse-to-Fine Question Answering for Long Documents. In Proc. of ACL Sunghwan Mac Kim, Qiongkai Xu, Lizhen Qu, Stephen Wan, Cecile Paris Demographic Inference on Twitter using Recursive Neural Networks. In Proc. ACL Yi Yang, Jacob Eisenstein Overcoming Language Variation in Sentiment Analysis with Social Attention. TACL. Chunting Zhou, Graham Neubig Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction. In Proc. of ACL 2017.

32 Appendix

33 (1) Oral 5 ( 5~7 ) Poster 3 10 ( )

34 (2) Poster ( )

35 ( ) Outstanding Papers 2 ( ) arxiv

Haiku Generation Based on Motif Images Using Deep Learning Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura Scho

Haiku Generation Based on Motif Images Using Deep Learning Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura Scho Haiku Generation Based on Motif Images Using Deep Learning 1 2 2 2 Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura 2 1 1 School of Engineering Hokkaido University 2 2 Graduate

More information

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

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?

More information

一般社団法人電子情報通信学会 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGIN

一般社団法人電子情報通信学会 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGIN 一般社団法人電子情報通信学会 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS 信学技報 IEICE Technical Report SP2019-12(2019-08)

More information

_314I01BM浅谷2.indd

_314I01BM浅谷2.indd 587 ネットワークの表現学習 1 1 1 1 Deep Learning [1] Google [2] Deep Learning [3] [4] 2014 Deepwalk [5] 1 2 [6] [7] [8] 1 2 1 word2vec[9] word2vec 1 http://www.ai-gakkai.or.jp/my-bookmark_vol31-no4 588 31 4 2016

More information

21 Pitman-Yor Pitman- Yor [7] n -gram W w n-gram G Pitman-Yor P Y (d, θ, G 0 ) (1) G P Y (d, θ, G 0 ) (1) Pitman-Yor d, θ, G 0 d 0 d 1 θ Pitman-Yor G

21 Pitman-Yor Pitman- Yor [7] n -gram W w n-gram G Pitman-Yor P Y (d, θ, G 0 ) (1) G P Y (d, θ, G 0 ) (1) Pitman-Yor d, θ, G 0 d 0 d 1 θ Pitman-Yor G 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

More information

名称未設定

名称未設定 NAACL-HLT 2012, 1 2012 6 3 8 NAACL-HLT 2012 (North American Chapter of ACL: Human Language Technologies) ACL Anthology 1 2 NAACL ACL (Association for Computational Linguistics) 2000 2001 2 ACL HLT 2003

More information

Computational Semantics 1 category specificity Warrington (1975); Warrington & Shallice (1979, 1984) 2 basic level superiority 3 super-ordinate catego

Computational Semantics 1 category specificity Warrington (1975); Warrington & Shallice (1979, 1984) 2 basic level superiority 3 super-ordinate catego Computational Semantics 1 category specificity Warrington (1975); Warrington & Shallice (1979, 1984) 2 basic level superiority 3 super-ordinate category preservation 1 / 13 analogy by vector space Figure

More information

A Japanese Word Dependency Corpus ÆüËܸì¤Îñ¸ì·¸¤ê¼õ¤±¥³¡¼¥Ñ¥¹

A Japanese Word Dependency Corpus   ÆüËܸì¤Îñ¸ì·¸¤ê¼õ¤±¥³¡¼¥Ñ¥¹ A Japanese Word Dependency Corpus 2015 3 18 Special thanks to NTT CS, 1 /27 Bunsetsu? What is it? ( ) Cf. CoNLL Multilingual Dependency Parsing [Buchholz+ 2006] (, Penn Treebank [Marcus 93]) 2 /27 1. 2.

More information

Microsoft PowerPoint - ACL2016_report2.pptx

Microsoft PowerPoint - ACL2016_report2.pptx ACL 2016 参加報告 ~ 会議概要と研究傾向分析 ~ 笹野遼平 ( 東工大 ) Contents ACL 2016 について 投稿 / 査読等に関する詳細 採択論文の傾向 2016/09/09 ACL2016 参加報告 ( 笹野 ) 2 ACL とは The annual meeting of the Association for Computational Linguistics (ACL)

More information

( : A8TB2163)

( : A8TB2163) 2011 2012 3 26 ( : A8TB2163) ( A B [1] A B A B B i 1 1 2 3 2.1... 3 2.1.1... 3 2.1.2... 4 2.2... 5 3 7 3.1... 7 3.2... 7 3.3 A B... 7 4 8 4.1... 8 4.1.1... 9 4.1.2... 9 4.1.3... 9 4.1.4... 10 4.2 A B...

More information

,,, Twitter,,, ( ), 2. [1],,, ( ),,.,, Sungho Jeon [2], Twitter 4 URL, SVM,, , , URL F., SVM,, 4 SVM, F,.,,,,, [3], 1 [2] Step Entered

,,, Twitter,,, ( ), 2. [1],,, ( ),,.,, Sungho Jeon [2], Twitter 4 URL, SVM,, , , URL F., SVM,, 4 SVM, F,.,,,,, [3], 1 [2] Step Entered DEIM Forum 2016 C5-1 182-8585 1-5-1 E-mail: saitoh-ryoh@uec.ac.jp, terada.minoru@uec.ac.jp Twitter,, Twitter,,, Bag of Words, Latent Semantic Indexing,.,,,, Twitter,, Twitter,, 1. SNS, SNS Twitter 1,,,

More information

NAACL2018参加報告

NAACL2018参加報告 NAACL2018 参加報告 東京工業大学上垣外英剛 1 NAACL2018 自己紹介 上垣外英剛 ( かみがいとひでたか ) 東工大高村研で博士取得 (2017/3) 昨年まで NTT コミュニケーション科学基礎研究所でポスドク (2017/4~2018/3) 現在東工大奥村研助教 (2018/4~) 今まで行ってきた研究機械翻訳 自動要約 句構造解析 2 NAACL2018 自己紹介 上垣外英剛

More information

( : A9TB2096)

( : A9TB2096) 2012 2013 3 31 ( : A9TB2096) Twitter i 1 1 1.1........................................... 1 1.2........................................... 1 2 4 2.1................................ 4 2.2...............................

More information

自然言語処理24_705

自然言語処理24_705 nwjc2vec: word2vec nwjc2vec nwjc2vec nwjc2vec 2 nwjc2vec 7 nwjc2vec word2vec nwjc2vec: Word Embedding Data Constructed from NINJAL Web Japanese Corpus Hiroyuki Shinnou, Masayuki Asahara, Kanako Komiya

More information

IPSJ SIG Technical Report Vol.2017-SLP-115 No /2/18 1,a) 1 1,2 Sakriani Sakti [1][2] [3][4] [5][6][7] [8] [9] 1 Nara Institute of Scie

IPSJ SIG Technical Report Vol.2017-SLP-115 No /2/18 1,a) 1 1,2 Sakriani Sakti [1][2] [3][4] [5][6][7] [8] [9] 1 Nara Institute of Scie 1,a) 1 1,2 Sakriani Sakti 1 1 1 1. [1][2] [3][4] [5][6][7] [8] [9] 1 Nara Institute of Science and Technology 2 Japan Science and Technology Agency a) ishikawa.yoko.io5@is.naist.jp 2. 1 Belief-Desire theory

More information

( )

( ) B4IM2035 2017 2 10 ( ) (e.g., eat ) (e.g., arrest ),,, 10., B4IM2035, 2017 2 i 1 1 2 3 2.1................. 3 2.2........ 3 3 5 3.1.... 5 3.2 DCS Vector.............................. 6 3.3 DCS Vector.......

More information

PowerPoint プレゼンテーション

PowerPoint プレゼンテーション 自然言語処理分野の 最前線 進藤裕之奈良先端科学技術大学院大学 2017-03-12 第五回ステアラボ AI セミナー 進藤裕之 (Hiroyuki Shindo) 所属 : 奈良先端科学技術大学院大学自然言語処理学研究室 ( 松本研 ) 助教 専門 : 構文解析, 意味解析 @hshindo (Github) 1 これまでの取り組み 文の文法構造 意味構造の導出 構文解析 複単語表現解析 述語項構造解析

More information

jpaper : 2017/4/17(17:52),,.,,,.,.,.,, Improvement in Domain Specific Word Segmentation by Symbol Grounding suzushi tomori, hirotaka kameko, takashi n

jpaper : 2017/4/17(17:52),,.,,,.,.,.,, Improvement in Domain Specific Word Segmentation by Symbol Grounding suzushi tomori, hirotaka kameko, takashi n ,,.,,,.,.,.,, Improvement in Domain Specific Word Segmentation by Symbol Grounding suzushi tomori, hirotaka kameko, takashi ninomiya, shinsuke mori and yoshimasa tsuruoka We propose a novel framework for

More information

3807 (3)(2) ,267 1 Fig. 1 Advertisement to the author of a blog. 3 (1) (2) (3) (2) (1) TV 2-0 Adsense (2) Web ) 6) 3

3807 (3)(2) ,267 1 Fig. 1 Advertisement to the author of a blog. 3 (1) (2) (3) (2) (1) TV 2-0 Adsense (2) Web ) 6) 3 Vol. 52 No. 12 3806 3816 (Dec. 2011) 1 1 Discovering Latent Solutions from Expressions of Dissatisfaction in Blogs Toshiyuki Sakai 1 and Ko Fujimura 1 This paper aims to find the techniques or goods that

More information

ACL2017-suzukake_MIURA

ACL2017-suzukake_MIURA ACL2017 読み会 @ すずかけ台 三浦康秀, 2017/9/19 富 ゼロックス株式会社 東京 業 学 紹介する論 Semantic Parsing for Question Answering 従来 い複雑な質問 紹介論 単純な関連する複数の質問 データセットの作成 + ニューラルモデルの提案 選択理由 Deep Reinforcement Learning Atari のゲームを対象とした

More information

IPSJ-TOD

IPSJ-TOD Vol. 3 No. 2 91 101 (June 2010) 1 1 1 2 1 TSC2 Automatic Evaluation of Text Summaries by Using Paraphrase Kazuho Hirahara, 1 Hidetsugu Nanba, 1 Toshiyuki Takezawa 1 and Manabu Okumura 2 The evaluation

More information

03_特集2_3校_0929.indd

03_特集2_3校_0929.indd MEDICAL IMAGING TECHNOLOGY Vol. 35 No. 4 September 2017 187 CT 1 1 convolutional neural network; ConvNet CT CT ConvNet 2D ConvNet CT ConvNet CT CT Med Imag Tech 35 4 : 187 193, 2017 1. CT MR 1 501-1194

More information

SICE東北支部研究集会資料(2017年)

SICE東北支部研究集会資料(2017年) 307 (2017.2.27) 307-8 Deep Convolutional Neural Network X Detecting Masses in Mammograms Based on Transfer Learning of A Deep Convolutional Neural Network Shintaro Suzuki, Xiaoyong Zhang, Noriyasu Homma,

More information

English for Specific Purposes

English for Specific Purposes 2013 LET 2013.10.12. TBLT email: urano@hgu.jp/ twitter: @uranoken p. 18 http://bit.ly/let_kansai2013b English for Specific Purposes English for Specific Purposes E S P E S P E S P ESP ESP ESP ESP TBLT

More information

¥ì¥·¥Ô¤Î¸À¸ì½èÍý¤Î¸½¾õ

¥ì¥·¥Ô¤Î¸À¸ì½èÍý¤Î¸½¾õ 2013 8 18 Table of Contents = + 1. 2. 3. 4. 5. etc. 1. ( + + ( )) 2. :,,,,,, (MUC 1 ) 3. 4. (subj: person, i-obj: org. ) 1 Message Understanding Conference ( ) UGC 2 ( ) : : 2 User-Generated Content [

More information

OSS

OSS 1 2 3 http://voicelabs.co 4 5 6 7 次 は 新金岡 新金岡 です 名詞 助詞 固有名詞 固有名詞 助動詞 ツギ ワ シンカナオカ シンカナオカ デス * * * ツギ ワ シンカナオカ シンカナオカ デス * * * DNN 1 1 1 1 1 2 1 2 3 1 2 4 1 2 6 T frames 8 9 この部分を見てみる 10 11 12 13 Synthesis

More information

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

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 Ver.2 Twitter 1,a) 1 1 2 2 1 100 Ver.2 2 Ver.2 264 Twitter 8 38,576 ver.2 Twitter word2vectwitter 1. Mikolov word2vec [1], [2], [3]Le Mikolov [4] Association for Computer Linguistics 2013 Twitter SemEval

More information

-----------------------------------------------------------------------------------------1 --------------------------------------------------------------------------------------1 -------------------------------------------------------------------------------------1

More information

untitled

untitled DEIM Forum 2019 I2-4 305-8573 1-1-1 305-8573 1-1-1 305-8573 1-1-1 ( ) 151-0053 1-3-15 6F 101-8430 2-1-2 CNN LSTM,,,, Measuring Beginner Friendliness / Visiual Intelligibility of Web Pages explaining Academic

More information

IPSJ SIG Technical Report Vol.2013-CVIM-187 No /5/30 1,a) 1,b), 1,,,,,,, (DNN),,,, 2 (CNN),, 1.,,,,,,,,,,,,,,,,,, [1], [6], [7], [12], [13]., [

IPSJ SIG Technical Report Vol.2013-CVIM-187 No /5/30 1,a) 1,b), 1,,,,,,, (DNN),,,, 2 (CNN),, 1.,,,,,,,,,,,,,,,,,, [1], [6], [7], [12], [13]., [ ,a),b),,,,,,,, (DNN),,,, (CNN),,.,,,,,,,,,,,,,,,,,, [], [6], [7], [], [3]., [8], [0], [7],,,, Tohoku University a) omokawa@vision.is.tohoku.ac.jp b) okatani@vision.is.tohoku.ac.jp, [3],, (DNN), DNN, [3],

More information

単語の分散表現と構成性の計算モデルの発展

単語の分散表現と構成性の計算モデルの発展 自然言語処理における深層ニューラルネットワーク 東北大学大学院情報科学研究科岡崎直観 (okazaki@ecei.tohoku.ac.jp) http://www.chokkan.org/ @chokkanorg 2016-06-09 言語処理における深層ニューラルネットワーク 1 自然言語処理とは 言葉を操る賢いコンピュータを作る 応用 : 情報検索, 機械翻訳, 質問応答, 自動要約, 対話生成,

More information

関連研究,.,,,.,Kaplan social present/ media richness, self presentation/self disclosure 6, 10 [Kaplan 10].,Kietzmann, identity, convers

関連研究,.,,,.,Kaplan social present/ media richness, self presentation/self disclosure 6, 10 [Kaplan 10].,Kietzmann, identity, convers AI AKB48 89 エンターテイメントにおける AI AI 的 AKB48 論 An AI Approach to Analyze AKB48 松尾豊 Yutaka Matsuo Graduate School of Engineering, The University of Tokyo. matsuo@weblab.t.u-tokyo.ac.jp 吉田宏司 Koji Yoshida yoshida@weblab.t.u-tokyo.ac.jp

More information

main.dvi

main.dvi Vol. 44 No. 11 Nov. 2003 2 (1) (2) Exploring Transfer Errors in Lexical and Structural Paraphrasing Atsushi Fujita and Kentaro Inui In lexical and structural paraphrasing, meaning-preserving linguistic

More information

PowerPoint プレゼンテーション

PowerPoint プレゼンテーション EMNLP 2014 参加報告 鶴岡研 M2 橋本和真 目次 1. 全体的な話 2. 発表を聞いてきた話 3. 自分の発表の話 4. どうでもいい話 目次 1. 全体的な話 2. 発表を聞いてきた話 3. 自分の発表の話 4. どうでもいい話 論文投稿数トップ 3 Semantics が機械翻訳よりも多くなった? 1. Semantics (! 2. Machine translation ( さすが

More information

WHITE PAPER RNN

WHITE PAPER RNN WHITE PAPER RNN ii 1... 1 2 RNN?... 1 2.1 ARIMA... 1 2.2... 2 2.3 RNN Recurrent Neural Network... 3 3 RNN... 5 3.1 RNN... 6 3.2 RNN... 6 3.3 RNN... 7 4 SAS Viya RNN... 8 4.1... 9 4.2... 11 4.3... 15 5...

More information

114 583/4 2012

114 583/4 2012 5-5 Fundamental Language Resources HASHIMOTO Chikara, Jong-Hoon Oh, SANO Motoki, and KAWADA Takuya Fundamental language resources are classifi ed into natural language processing tools and natural language

More information

知能科学:ニューラルネットワーク

知能科学:ニューラルネットワーク 2 3 4 (Neural Network) (Deep Learning) (Deep Learning) ( x x = ax + b x x x ? x x x w σ b = σ(wx + b) x w b w b .2.8.6 σ(x) = + e x.4.2 -.2 - -5 5 x w x2 w2 σ x3 w3 b = σ(w x + w 2 x 2 + w 3 x 3 + b) x,

More information

知能科学:ニューラルネットワーク

知能科学:ニューラルネットワーク 2 3 4 (Neural Network) (Deep Learning) (Deep Learning) ( x x = ax + b x x x ? x x x w σ b = σ(wx + b) x w b w b .2.8.6 σ(x) = + e x.4.2 -.2 - -5 5 x w x2 w2 σ x3 w3 b = σ(w x + w 2 x 2 + w 3 x 3 + b) x,

More information

Vol. 9 No. 5 Oct. 2002 (?,?) 2000 6 5 6 2 3 6 4 5 2 A B C D 132

Vol. 9 No. 5 Oct. 2002 (?,?) 2000 6 5 6 2 3 6 4 5 2 A B C D 132 2000 6 5 6 :, Supporting Conference Program Production Using Natural Language Processing Technologies Hiromi itoh Ozaku Masao Utiyama Masaki Murata Kiyotaka Uchimoto and Hitoshi Isahara We applied natural

More information

kut-paper-template.dvi

kut-paper-template.dvi 14 Application of Automatic Text Summarization for Question Answering System 1030260 2003 2 12 Prassie Posum Prassie Prassie i Abstract Application of Automatic Text Summarization for Question Answering

More information

sequence to sequence, B3TB2006, i

sequence to sequence, B3TB2006, i B3TB2006 2017 3 31 sequence to sequence, B3TB2006, 2017 3 31. i A Study on a Style Control for Dialogue Response Generation Reina Akama Abstract We propose a new dialogue response generation model combining

More information

2

2 NTT 2012 NTT Corporation. All rights reserved. 2 3 4 5 Noisy Channel f : (source), e : (target) ê = argmax e p(e f) = argmax e p(f e)p(e) 6 p( f e) (Brown+ 1990) f1 f2 f3 f4 f5 f6 f7 He is a high school

More information

Twitter‡Ì”À‰µ…c…C†[…g‡ðŠŸŠp‡µ‡½…^…C…•…›…C…fi‘ã‡Ì…l…^…o…„‘îŁñ„�™m

Twitter‡Ì”À‰µ…c…C†[…g‡ðŠŸŠp‡µ‡½…^…C…•…›…C…fi‘ã‡Ì…l…^…o…„‘îŁñ„�™m 27 Twitter 1431050 2016 3 14 1 Twitter,,.,.,., Twitter,.,,.,,. URL,,,. BoW(Bag of Words), LSI(Latent Semantic Indexing)., URL,,,,., Accuracy, AUC(Area Under the Curve), Precision, Recall, F,. URL,,,.,

More information

Trial for Value Quantification from Exceptional Utterances 37-066593 1 5 1.1.................................. 5 1.2................................ 8 2 9 2.1.............................. 9 2.1.1.........................

More information

f ê ê = arg max Pr(e f) (1) e M = arg max λ m h m (e, f) (2) e m=1 h m (e, f) λ m λ m BLEU [11] [12] PBMT 2 [13][14] 2.2 PBMT Hiero[9] Chiang PBMT [X

f ê ê = arg max Pr(e f) (1) e M = arg max λ m h m (e, f) (2) e m=1 h m (e, f) λ m λ m BLEU [11] [12] PBMT 2 [13][14] 2.2 PBMT Hiero[9] Chiang PBMT [X 1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e) 1. Statistical Machine Translation: SMT[1] [2] [3][4][5][6] 2 Cascade Translation [3] Triangulation [7] Phrase-Based Machine Translation: PBMT[8] 1

More information

IPSJ SIG Technical Report Vol.2013-NL-214 No /11/15 1,a) (1) [ ] [ ] [14], [28] [17] 1 Tohoku University, Sendai, Miyagi 980 8

IPSJ SIG Technical Report Vol.2013-NL-214 No /11/15 1,a) (1) [ ] [ ] [14], [28] [17] 1 Tohoku University, Sendai, Miyagi 980 8 1,a) 2 2 3 4 5 3 1 1. (1) [ ] [ ] [14], [28] [17] 1 Tohoku University, Sendai, Miyagi 980 8579, Japan 2 Tokyo Institute of Technology 3 National Institute of Informatics 4 University of Yamanashi 5 Future

More information

自然言語処理23_175

自然言語処理23_175 2 Sequence Alignment as a Set Partitioning Problem Masaaki Nishino,JunSuzuki, Shunji Umetani, Tsutomu Hirao and Masaaki Nagata Sequence alignment, which involves aligning elements of two given sequences,

More information

% 2 3 [1] Semantic Texton Forests STFs [1] ( ) STFs STFs ColorSelf-Simlarity CSS [2] ii

% 2 3 [1] Semantic Texton Forests STFs [1] ( ) STFs STFs ColorSelf-Simlarity CSS [2] ii 2012 3 A Graduation Thesis of College of Engineering, Chubu University High Accurate Semantic Segmentation Using Re-labeling Besed on Color Self Similarity Yuko KAKIMI 2400 90% 2 3 [1] Semantic Texton

More information

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

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 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

More information

自然言語処理21_249

自然言語処理21_249 1,327 Annotation of Focus for Negation in Japanese Text Suguru Matsuyoshi This paper proposes an annotation scheme for the focus of negation in Japanese text. Negation has a scope, and its focus falls

More information

- 137 - - 138 - - 139 - Larsen-Freeman Teaching Language: From Grammar to Grammaring form meaning use "I will ~." Iwill - 140 - R. Ellis Task-based Language Learning and Teaching Long Swain - 141 - - 142

More information

IPSJ SIG Technical Report Vol.2019-MUS-123 No.23 Vol.2019-SLP-127 No /6/22 Bidirectional Gated Recurrent Units Singing Voice Synthesi

IPSJ SIG Technical Report Vol.2019-MUS-123 No.23 Vol.2019-SLP-127 No /6/22 Bidirectional Gated Recurrent Units Singing Voice Synthesi Bidirectional Gated Recurrent Units Singing Voice Synthesis Using Bidirectional Gated Recurrent Units. [] (HMM) [] [3], [4] Kobe University MEC Company Ltd. (Text to Speech: TTS) [5].. 3Hz Hz c 9 Information

More information

Fig. 2 28th Ryuou Tournament, Match 5, 59th move. The last move is Black s Rx5f. 1 Tic-Tac-Toe Fig. 1 AsearchtreeofTic-Tac-Toe. [2] [3], [4]

Fig. 2 28th Ryuou Tournament, Match 5, 59th move. The last move is Black s Rx5f. 1 Tic-Tac-Toe Fig. 1 AsearchtreeofTic-Tac-Toe. [2] [3], [4] 1,a) 2 3 2017 4 6, 2017 9 5 Predicting Moves in Comments for Shogi Commentary Generation Hirotaka Kameko 1,a) Shinsuke Mori 2 Yoshimasa Tsuruoka 3 Received: April 6, 2017, Accepted: September 5, 2017 Abstract:

More information

ニュラールネットに基づく機械翻訳 ニューラルネットに 基づく機械翻訳 Graham Neubig 奈良先端科学技術大学院大学 (NAIST)

ニュラールネットに基づく機械翻訳 ニューラルネットに 基づく機械翻訳 Graham Neubig 奈良先端科学技術大学院大学 (NAIST) ニューラルネットに 基づく機械翻訳 Graham Neubig 奈良先端科学技術大学院大学 (NAIST) 205-9-5 I am giving a talk at Kyoto University 私 は 京都 大学 で 講演 を しています ( 終 ) 2 次の単語確率を推測 F = I am giving a talk P(e= 私 F) = 0.8 P(e= 僕 F) = 0.03 P(e=

More information

137 Author s E-mail Address: torii@shoin.ac.jp Relationship between appearance modifying behavior and narcissistic tendency in Japanese males TORII Sakura Faculty of Human Sciences, Kobe Shoin Women s

More information

自然言語処理におけるDeep Learning

自然言語処理におけるDeep Learning 自然言語処理における Deep Learning 東北大学大学院情報科学研究科岡崎直観 (okazaki@ecei.tohoku.ac.jp) http://www.chokkan.org/ @chokkanorg 1 自然言語処理とは 言葉を操る賢いコンピュータを作る 応用 : 情報検索, 機械翻訳, 質問応答, 自動要約, 対話生成, 評判分析,SNS 分析, 基礎 : 品詞タグ付け ( 形態素解析

More information

完成卒論.PDF

完成卒論.PDF LAN 4 9920449 2 0 LAN Bluetooth LAN 1 LAN LAN LAN LAN 2 LAN Bluetooth LAN Bluetooth 3 Bluetooth 4 Bluetooth 5 Bluetooth Bluetooth 6 LAN Bluetooth LAN LocalAreaNetwork 1 LAN LAN LAN LAN Ethernet Ethernet

More information

IPSJ SIG Technical Report Vol.2015-BIO-44 No /12/7 1,a) 1,b) 1,c) ( ) CATH CATH BLAST PSI-BLAST LSA 1. DNA DNA 4 A( ) T( ) G( ) C( ) 20 A( ) E(

IPSJ SIG Technical Report Vol.2015-BIO-44 No /12/7 1,a) 1,b) 1,c) ( ) CATH CATH BLAST PSI-BLAST LSA 1. DNA DNA 4 A( ) T( ) G( ) C( ) 20 A( ) E( 1,a) 1,b) 1,c) ( ) CATH CATH BLAST PSI-BLAST LSA 1. DNA DNA 4 A( ) T() G( ) C( ) 20 A( ) E() F( ) KEPEQL...AVS α- β- apple, gravitation, formulate, by Newton was inspired to formulate gravitation by watching

More information

- 1-128 - 2 -

- 1-128 - 2 - 127 - 1-128 - 2 - - 3-129 - 4 - 2-5 - 130-6 - - 7-131 - 8 - - 9-132 - 10 - 6041 3 () 1 ( ) () 6041 (1010) 1041 (192) 1941 () 2 (1) (2) (3) () 3 1 1 () 4 2 () 5 1 2 3 4 () 6 () 7-11 - 133-12 - 134 135 136

More information

1 1 tf-idf tf-idf i

1 1 tf-idf tf-idf i 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

More information

音響モデル triphone 入力音声 音声分析 デコーダ 言語モデル N-gram bigram HMM の状態確率として利用 出力層 triphone: 3003 ノード リスコア trigram 隠れ層 2048 ノード X7 層 1 Structure of recognition syst

音響モデル triphone 入力音声 音声分析 デコーダ 言語モデル N-gram bigram HMM の状態確率として利用 出力層 triphone: 3003 ノード リスコア trigram 隠れ層 2048 ノード X7 層 1 Structure of recognition syst 1,a) 1 1 1 deep neural netowrk(dnn) (HMM) () GMM-HMM 2 3 (CSJ) 1. DNN [6]. GPGPU HMM DNN HMM () [7]. [8] [1][2][3] GMM-HMM Gaussian mixture HMM(GMM- HMM) MAP MLLR [4] [3] DNN 1 1 triphone bigram [5]. 2

More information

_AAMT/Japio特許翻訳研究会.key

_AAMT/Japio特許翻訳研究会.key 2017/02/10 D2 ( ) 2015 3 2015 4 ~ 2016 8~11 : 2016 11 ( )!? 11 Google+ = = ( + ) (NMT) 1 ( ) Google (Wu et al., 2016) NMT news test 2013 BLEU score ( ) (: http://homepages.inf.ed.ac.uk/rsennric/amta2016.pdf)

More information

1 Question 1

1 Question 1 1 Question 1 2 2 Question 3 3 Question 4 4 Question 5 5 Question 6 6 Question 7 Question 8 8 Question 9 9 Question 10 10 Question 11 11 Question 12 12 Question 13 13 Question 14 14 Question 15 15 Question

More information

橡100ninnokoe

橡100ninnokoe Question 1 Question 2 Question 3 Question 4 Question 5 Question 6 Question 7 Question 8 Question 1 Question 2 Question 3 Question 2 Question 1 Question 3 Question 5 Question 4 Question 6 Question 7

More information

[1] [2] [3] 1 GPS 1 Twitter *1 *1 GPS [4] [5] [6] 2 [7] 1 [8] Restricted Boltzmann Machine RBM RBM

[1] [2] [3] 1 GPS 1 Twitter *1 *1 GPS [4] [5] [6] 2 [7] 1 [8] Restricted Boltzmann Machine RBM RBM 1,a) 2, 1,b) 1,c) 3,d) 1,e) 2014 2 21, 2014 9 12 2 Automatic Generation of Shogi Commentary with a Log-linear Language Model Hirotaka Kameko 1,a) Makoto Miwa 2, 1,b) Yoshimasa Tsuruoka 1,c) Shinsuke Mori

More information

untitled

untitled 580 26 5 SP-G 2011 AI An Automatic Question Generation Method for a Local Councilor Search System Yasutomo KIMURA Hideyuki SHIBUKI Keiichi TAKAMARU Hokuto Ototake Tetsuro KOBAYASHI Tatsunori MORI Otaru

More information

1 Twitter Twitter Twitter 2. 1 Xu [3] Twitter Twitter Twitter Twitter iphone iphone iphone Twitter Xu [3] Twitter Xu [5] Web Web Web Web

1 Twitter Twitter Twitter 2. 1 Xu [3] Twitter Twitter Twitter Twitter iphone iphone iphone Twitter Xu [3] Twitter Xu [5] Web Web Web Web DEIM Forum 2015 G8-5 Twitter 305 8550 1 2 305 8550 1 2 E-mail: s1111509@u.tsukuba.ac.jp, yohei@slis.tsukuba.ac.jp n-gram (n=3 9) 18 Twitter 1. 1. 1 Twitter 1 Twitter Twitter Twitter Twitter 1https://twitter.com

More information

スマート都市監視を実現する富士通のDeep Learning技術

スマート都市監視を実現する富士通のDeep Learning技術 Deep Learning Fujitsu Deep Learning Technology that Enables Smart City Monitoring あらまし IP AI 2018 3 FUJITSU Technical Computing Solution GREENAGES Citywide Surveillance V2 Citywide Surveillance Deep Learning

More information

taro.watanabe at nict.go.jp

taro.watanabe at nict.go.jp taro.watanabe at nict.go.jp https://sites.google.com/site/alaginmt2014/ ... I want to study about machine translation. I need to master machine translation. machine translation want to study. infobox infobox

More information

Microsoft PowerPoint - SSII_harada pptx

Microsoft PowerPoint - SSII_harada pptx The state of the world The gathered data The processed data w d r I( W; D) I( W; R) The data processing theorem states that data processing can only destroy information. David J.C. MacKay. Information

More information

ディープラーニングとオープンサイエンス ~研究の爆速化が引き起こす摩擦なき情報流通へのシフト~

ディープラーニングとオープンサイエンス ~研究の爆速化が引き起こす摩擦なき情報流通へのシフト~ KITAMOTO Asanobu http://researchmap.jp/kitamoto/ KitamotoAsanob u 1 2 3 4 5 1. 2. 3. 6 Lawrence Lessig (Founder of Creative Commons), Code: And Other Laws of Cyber Space (first edition 1999) 7 NSF Data

More information

”Лï‡Æ™²“¸_‚æ4“ƒ__‘dflÅPDF‘‚‡«‘o‡µ.pdf

”Лï‡Æ™²“¸_‚æ4“ƒ__‘dflÅPDF‘‚‡«‘o‡µ.pdf No.4 1 1 2 No.4 No.4 2 3 4 5 6 No.4 7 8 9 No.4 10 3 No.4 No.4 4 No.4 No.4 1 2 3 4 5 6 7 8 9 10 Assessment Class and Conformity: A Study in Values, With a Reassessment Research in Social Stratification

More information

johnny-paper2nd.dvi

johnny-paper2nd.dvi 13 The Rational Trading by Using Economic Fundamentals AOSHIMA Kentaro 14 2 26 ( ) : : : The Rational Trading by Using Economic Fundamentals AOSHIMA Kentaro abstract: Recently Artificial Markets on which

More information

@08470030ヨコ/篠塚・窪田 221号

@08470030ヨコ/篠塚・窪田 221号 Abstract Among three distinctive types of Japanese writing systems Kanji, Hiragana and Katakana, a behavioral experiment using 97 university students as subjects implies that Katakana is regarded as most

More information

†sŸ_Ł¶†t›ÍŠlŁª(P26†`)/−Ø“‚‡É‡¨‡¯‡é…}…j…t…F…X…gŁ†‰y†`

†sŸ_Ł¶†t›ÍŠlŁª(P26†`)/−Ø“‚‡É‡¨‡¯‡é…}…j…t…F…X…gŁ†‰y†` policy change policy transfer Dolowitz prospective policy evaluationmossberger and Wolman Rose 26 XLVIII DolowitzDolowitz and Marsh analogical model EvansEvans and Davies Methodology Policy community Epistemic

More information

1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2

1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2 CHLAC 1 2 3 3,. (CHLAC), 1).,.,, CHLAC,.,. Suspicious Behavior Detection based on CHLAC Method Hideaki Imanishi, 1 Toyohiro Hayashi, 2 Shuichi Enokida 3 and Toshiaki Ejima 3 We have proposed a method for

More information

白井学習法(1).ppt

白井学習法(1).ppt a Grammar Translation Method 19 1940~1960 Audiolingual Method - structural linguistics - behaviorism stimulus-response reinforcement habit formation (e.g., Skinner, 1957) 2 - L1 L2 [contrastive analysis]

More information

[4], [5] [6] [7] [7], [8] [9] 70 [3] 85 40% [10] Snowdon 50 [5] Kemper [3] 2.2 [11], [12], [13] [14] [15] [16]

[4], [5] [6] [7] [7], [8] [9] 70 [3] 85 40% [10] Snowdon 50 [5] Kemper [3] 2.2 [11], [12], [13] [14] [15] [16] 1,a) 1 2 1 12 1 2Type Token 2 1 2 1. 2013 25.1% *1 2012 8 2010 II *2 *3 280 2025 323 65 9.3% *4 10 18 64 47.6 1 Center for the Promotion of Interdisciplinary Education and Research, Kyoto University 2

More information

y y=2 x x Dialogue Language Education & Technology, 44 The Journal of Physiology, 232 (2) The Age Factor in Second Language Acquisition Studies in Second Language Acquisition, 19 (4) Flashbulb Memories

More information

2014/1 Vol. J97 D No. 1 2 [2] [3] 1 (a) paper (a) (b) (c) 1 Fig. 1 Issues in coordinating translation services. (b) feast feast feast (c) Kran

2014/1 Vol. J97 D No. 1 2 [2] [3] 1 (a) paper (a) (b) (c) 1 Fig. 1 Issues in coordinating translation services. (b) feast feast feast (c) Kran a) b) c) Improving Quality of Pivot Translation by Context in Service Coordination Yohei MURAKAMI a), Rie TANAKA b),andtoruishida c) Web 1. Web 26.8% 30.9% 21.3% 21% 1 n n(n 1) Department of Social Informatics,

More information

IPSJ SIG Technical Report On a Bayesian Network-based Model for Referring Expressions Kotaro Funakoshi, 1 Mikio Nakano, 1 Takenobu Tokunaga 2

IPSJ SIG Technical Report On a Bayesian Network-based Model for Referring Expressions Kotaro Funakoshi, 1 Mikio Nakano, 1 Takenobu Tokunaga 2 1 1 2 2 On a Bayesian Network-based Model for Referring Expressions Kotaro Funakoshi, 1 Mikio Nakano, 1 Takenobu Tokunaga 2 and Ryu Iida 2 A Bayesian network-based model available both for resolution and

More information

main.dvi

main.dvi 305 8550 1 2 CREST fujii@slis.tsukuba.ac.jp 1 7% 2 2 3 PRIME Multi-lingual Information Retrieval 2 2.1 Cross-Language Information Retrieval CLIR 1990 CD-ROM a. b. c. d. b CLIR b 70% CLIR CLIR 2.2 (b) 2

More information

,., ping - RTT,., [2],RTT TCP [3] [4] Android.Android,.,,. LAN ACK. [5].. 3., 1.,. 3 AI.,,Amazon, (NN),, 1..NN,, (RNN) RNN

,., ping - RTT,., [2],RTT TCP [3] [4] Android.Android,.,,. LAN ACK. [5].. 3., 1.,. 3 AI.,,Amazon, (NN),, 1..NN,, (RNN) RNN DEIM Forum 2018 F1-1 LAN LSTM 112 8610 2-1-1 163-8677 1-24-2 E-mail: aoi@ogl.is.ocha.ac.jp, oguchi@is.ocha.ac.jp, sane@cc.kogakuin.ac.jp,,.,,., LAN,. Android LAN,. LSTM LAN., LSTM, Analysis of Packet of

More information

untitled

untitled The 23rd Annual Meeting of the Japanese Association of Cardiac Rehabilitation! The 23rd Annual Meeting of the Japanese Association of Cardiac Rehabilitation The 23rd Annual Meeting of the Japanese Association

More information

untitled

untitled 12 20 10 14 00 12 43 2006 1 1994 1994 14 2 14 24 115 40 85 47 24 14 3 1990 17.4 47 8.1 1995 5.7 95 4 15 20 20 22 24 806 18 700 65 24 26 3,000 1990 2007 20 47 20 20 2007 28.2 5 1980 85 90 95 2000 1980 10

More information

QW-3414

QW-3414 MA1312-C P 1 2 3 A E L D E D A A E D A D D D D D E A C A C E D A A A C A C A C E E E D D D A C A C A A A A C A C A C E E C C E D D C C C E C E C C E C C C E D A C A C A C E L B B

More information

1 Fogg Fogg Behavior Model [1] information cascade [2] TPO [3] Fig. 2 Target area of this paper. 1 Fig. 1 Fogg b

1 Fogg Fogg Behavior Model [1] information cascade [2] TPO [3] Fig. 2 Target area of this paper. 1 Fig. 1 Fogg b 1,a) 1 1 1 2014 9 20, 2015 1 5 TPO Extracting Purpose-for-Action to Enhance Local Information Service Noriko Yokoyama 1,a) Kaname Funakoshi 1 Hiroyuki Toda 1 Yoshimasa Koike 1 Received: September 20, 2014,

More information

!

! The 22nd Annual Meeting of the Japanese Association of Cardiac Rehabilitation ! The 22nd Annual Meeting of the Japanese Association of Cardiac Rehabilitation The 22nd Annual Meeting of the Japanese Association

More information