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ç·åˆç ”究大å¦é™¢å¤§å¦ã€€è¤‡åˆç§‘å¦ç ”究科　 æƒ…å ±å¦å°‚攻　å’　åšå£«ï¼ˆæƒ…å ±å¦ï¼‰ 自然言語処ç†ã‚„機械å¦ç¿’ã€ãƒ‡ãƒ¼ã‚¿åˆ†æžã«é–¢ã™ã‚‹ç ”究内容ã¨webシステムã®é–‹ç™ºã¨é‹ç”¨ã«ã¤ã„ã¦æ›¸ã„ã¦ã„ã¾ã™ã€‚ シリコンãƒãƒ¬ãƒ¼ãƒ™ãƒ³ãƒãƒ£ãƒ¼ã¿ãŸã„ã«æ·±ã„技術ã®äº‹æ¥åŒ–ã‚’ã—ãŸã„ã¨æ€ã£ã¦ã„ã¾ã™ã€‚ ã”興味ã‚ã‚‹æ–¹ã¯ã”連絡ãã ã•ã„。 word2vecã®å‹‰å¼·ã—ãªã„ã¨ã„ã‘ãªã„ã¨æ€ã£ãŸã®ã§ã€ Efficient Estimation of Word Representations in Vector Space. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Google Inc. In Proceedings of Workshop at ICLR, 2013. We propose two novel model architectures for computing contin
Fast and Robust Neural Network Joint Models for Statistical Machine Translation
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 1370–1380, Baltimore, Maryland, USA, June 23-25 2014. c 2014 Association for Computational Linguistics Fast and Robust Neural Network Joint Models for Statistical Machine Translation Jacob Devlin, Rabih Zbib, Zhongqiang Huang, Thomas Lamar, Richard Schwartz, and John Makhoul Raytheon BBN Technologies, 10
ACL2014èªã¿ä¼šï¼šFast and Robust Neural Network Joint Models for Statistical Machine Translation
ACL2014èªã¿ä¼šï¼šFast and Robust Neural Network Joint Models for Statistical Machine Translation 1. Fast and Robust Neural Network Joint Models for Statistical Machine Translation Authors: Jacob Devlin, Rabih Zbib, Zhongqiang Huang, Thomas Lamar, Richard Schwartz, and John Makhoul èªã‚€äººï¼šå¾³æ°¸ 2014/07/12 ACL2014èªã¿ä¼š@PFI
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Python ã§ä¸€ç•ªæœ€åˆã«å…¥ã‚Œã‚‹ã¹ãパッケージ setuptools 㨠pip
追記 20170227: pip 㯠Python 3.4 以é™ã§ã¯ Python 本体ã«åŒæ¢±ã•ã‚Œã‚‹ã‚ˆã†ã«ãªã£ãŸã¨ã®ã“ã¨ã§ã€ä»¥ä¸‹ã®ä½œæ¥ã¯ä¸è¦ã«ãªã‚Šã¾ã—ãŸã€‚Python 本体をインストールã™ã‚Œã° pip ãŒãƒ¢ã‚¸ãƒ¥ãƒ¼ãƒ«ã¨ã—ã¦æ¬¡ã®å½¢å¼ã§ã™ãã«åˆ©ç”¨ã§ãã¾ã™ã€‚ 次ã®ãƒšãƒ¼ã‚¸ãŒå‚考ã«ãªã‚Šã¾ã™ã€‚ Python モジュールã®ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ« 注: ã“ã“ã§ã¯ setuptools を紹介ã—ã¦ã¾ã™ãŒã€ç¾åœ¨ setuptools ã¯æ›´æ–°ãŒã‚¹ãƒˆãƒƒãƒ—ã—ã¦ã„ã¾ã™ã€‚代ã‚ã‚Šã« distribute ã¨ã„ã†ãƒ‘ッケージãŒé–‹ç™ºã•ã‚Œã¦ã„ã¦ã€ãれ㌠setuptools ã®ä»£ã‚ã‚Šã«ãªã‚‹ã®ã§ã€ä»¥ä¸‹ã€ setuptools ã®éƒ¨åˆ†ã‚’ distribute ã§èªã¿æ›¿ãˆã¦ã„ãŸã ãã¨ã‚ˆã„ã‹ã¨æ€ã„ã¾ã™ã€‚基本的ãªä½¿ã„æ–¹ã¯åŒã˜ã§ã™ã€‚ Python ã®é…力ã®ã²ã¨ã¤ã«ã€ä¸–ç•Œä¸ã§é–‹ç™ºã•ã‚Œã¦ã„る豊富ãªãƒ‘ッケージ（ライブラリ）群ãŒã‚ã‚Šã¾ã™ã€‚ ã™ã°ã‚‰ã—ã„パッケージ
NIPS2013èªã¿ä¼šã§word2vecè«–æ–‡ã®ç´¹ä»‹ã‚’ã—ã¾ã—ãŸ
先週〠@sla ã•ã‚“主催ã®NIPS2013èªã¿ä¼šã§ã€word2vec論文（æ£ç¢ºã«ã¯ç¶šå ±ï¼‰ã®ç´¹ä»‹ã‚’ã—ã¾ã—ãŸã€‚ ã¡ã‚‡ã£ã¨è§£èª¬ã‚’書ãã¾ã™ã€‚ ã“ã®ã¨ã“ã‚ã®æ·±å±¤å¦ç¿’ブームã¯è‡ªç„¶è¨€èªžå‡¦ç†ã«ã‚‚æ¥ã¦ã„ã¦ã€ãれらã®ã‚¦ãƒã®1ã¤ã¨è¨€ã‚ã‚Œã¦ã„ã¾ã™ï¼ˆãŒã€å…¨ç„¶deepã£ã½ã•ã¯ãªã„）。 最åˆã®ãƒ¢ãƒãƒ™ãƒ¼ã‚·ãƒ§ãƒ³ãŒã©ã†ã„ã†ã¨ã“ã‚ã«ã‚ã£ãŸã‹ã¨ã„ã†ã®ã¯ã€ã¡ã‚‡ã£ã¨è‰²ã€…ã ã¨æ€ã„ã¾ã™ãŒï¼ˆãŠãらã最åˆã¯è¨€èªžãƒ¢ãƒ‡ãƒ«ã«ãŠã‘ã‚‹ä½Žé »åº¦èªžã®ç¢ºçŽ‡ã‚’ウマイã“ã¨ãƒ¢ãƒ‡ãƒ«åŒ–・推定ã—ãŸã‹ã£ãŸã‚“ã§ã¯ãªã„ã‹ãªï¼‰ã€ä½•ã¯ã¨ã‚‚ã‚ã‚Œå˜èªžã®æ„味的ãªã‚ã‚‹ã„ã¯çµ±èªžçš„ãªæŒ¯ã‚‹èˆžã„をベクトル表ç¾ã§è¡¨ã™ã¨ã„ã†ç ”究ãŒæµè¡Œã£ã¦ãŠã‚Šã¾ã™ã€‚ ベクトル表ç¾ã¨ã„ã†ã®ã¯ã€1ã¤ã®å˜èªžwã«å¯¾ã—ã¦ã€ãã®å˜èªžã‚’「表ç¾ã€ã™ã‚‹ã‚ˆã†ãªãƒ™ã‚¯ãƒˆãƒ« v(w) を作りã¾ã™ã€‚ ãã‚“ãªã“ã¨ã„ã‚ã‚Œã¦ã‚‚ã€ä½œã‚Œã°ï¼Ÿã¨ã„ã†ã“ã¨ãªã‚“ã§ã™ãŒã€ã§ããŸãƒ™ã‚¯ãƒˆãƒ«ã«å¯¾ã—ã¦ä½•ã‹ã—ら「都åˆã®ã„ã„ã€æ€§è³ªãŒã§ãã‚‹ã“ã¨ãŒçœŸã®ç›®æ¨™ã§ã™ã€‚ 「都åˆã®ã„ã„ã€
Statistical Semantic入門 ~分布仮説ã‹ã‚‰word2vecã¾ã§~
1. The document discusses various statistical and neural network-based models for representing words and modeling semantics, including LSI, PLSI, LDA, word2vec, and neural network language models. 2. These models represent words based on their distributional properties and contexts using techniques like matrix factorization, probabilistic modeling, and neural networks to learn vector representatio
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Distributed Representations of Sentences and Documents
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