Statistical Semantic入門~分布仮説ã‹ã‚‰word2vecã¾ã§ #1
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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
18 Matrix decomposition and latent semantic indexing (pp.369-384) - シリコンã®è°·ã®ã‚¾ãƒ³ãƒ“
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トピックモデルã®LDA(Latent Dirichlet Allocation)ãŒé›£ã—ã„ã£ã¦è©±ã‚’よãèžãã¾ã™ã‘ã©ï¼Œè©³ã—ã„ç†è«–çš„ãªéƒ¨åˆ†ã¯ã¨ã‚‚ã‹ãã©ã†ã„ã†æµã‚Œã«ä½ç½®ã™ã‚‹ã‚‚ã®ã‹ã£ã¦ã®ã¯ã‚ã‹ã‚Šã‚„ã™ã„ã®ã§ç°¡å˜ã«ãƒ¡ãƒ¢ï¼Ž å°‚é–€ã§ãªã„ã®ã§ï¼Œè©³ã—ãã¯å‚考文献をèªã‚“ã ã»ã†ãŒã„ã„ã§ã™ï¼Ž トピック åŒã˜æ–‡æ›¸å†…ã§ä½¿ã‚れる確率ãŒé«˜ã„よã†ãªä¼¼ãŸæ„味をæŒã¤å˜èªžã®é›†ã¾ã‚Šï¼Ž 例ãˆã°ã‚¹ãƒãƒ¼ãƒ„トピックãªã‚‰ã€Œé‡Žçƒã€ã€Œã‚µãƒƒã‚«ãƒ¼ã€ã€Œãƒœãƒ¼ãƒ«ã€ãŒå‡ºã‚„ã™ã„，ãªã©ï¼Ž トピックモデルã¯æ–‡æ›¸ã®ãƒˆãƒ”ックã¨ï¼Œãƒˆãƒ”ックã«å±žã™ã‚‹å˜èªžã‚’推定ã™ã‚‹ï¼Ž å˜èªžé »åº¦ã‹ã‚‰ãƒˆãƒ”ックモデルã¾ã§ã®æµã‚Œã®ã‚¤ãƒ¡ãƒ¼ã‚¸ 文書をモデル化ã—よã†â†’å˜èªžã®é »åº¦ åŒç¾©èªžã‚„多義語をæ‰ãˆã‚ˆã†â‰’次元削減をã—よã†â†’LSAï¼SVD→以下ãŒãƒˆãƒ”ックモデル 確率的ã«ã—よã†â†’PLSI ベイズ的ã«ã—よã†ï¼è¨“練データã«ç™»å ´ã—ãªã‹ã£ãŸã‚‚ã®ã«ã‚‚対応ã§ãる→LDA トピック数ãŒè‡ªå‹•ã§æ±ºã¾ã‚‹ã‚ˆã†ã«ã—よã†â†’ノンパラメトリ
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Gensim: topic modelling for humans
Google Code Archive - Long-term storage for Google Code Project Hosting.
Dimensionality reduction for sparse binary data - FastML
Google Code Archive - Long-term storage for Google Code Project Hosting.
http://blog.yuku-t.com/entry/20110623/1308810518
潜在的æ„味インデã‚シング（LSI）徹底入門 - ã‚らã³ã日記
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