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word2vecã«è‹±è¾žéƒŽãƒ‡ãƒ¼ã‚¿ã‚’放り込んã§ã¿ãŸ - naoya_t@hatenablog
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Moses - Main/HomePage
1. Moses Overview Manual Online Demos FAQ Mailing Lists Get Involved Recent Changes 2. Getting Started Source Installation Baseline System Packages Releases Sample Data Links to Corpora 3. Tutorials Phrase-Based Tutorial Syntax Tutorial Factored Tutorial Optimizing Moses Experiment.Perl 4. Training Overview Prepare training data Factored Training 1 Prepare data 2 Run GIZA 3 Align words 4 Lexical t
Gensim: topic modelling for humans
✔   Train large-scale semantic NLP models ✔   Represent text as semantic vectors ✔   Find semantically related documents from gensim import corpora, models, similarities, downloader # Stream a training corpus directly from S3. corpus = corpora.MmCorpus("s3://path/to/corpus") # Train Latent Semantic Indexing with 200D vectors. lsi = models.LsiModel(corpus, num_topics=200) # Convert another corpus t
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Liang Huang's CUNY Homepage
Assistant Professor, Department of Computer Science, Queens College Doctoral Faculty, Computer Science Program, The Graduate Center The City University of New York My research interests are mainly in the algorithmic and formal aspects of computational linguistics (esp. parsing and machine translation) and artificial intelligence in general. The key questions that motivate my research are: Why ar
Apache Stanbol - Welcome to Apache Stanbol!
This project has retired. For details please refer to its Attic page. Welcome to Apache Stanbol! Apache Stanbol provides a set of reusable components for semantic content management. Apache Stanbol's intended use is to extend traditional content management systems with semantic services. Other feasible use cases include: direct usage from web applications (e.g. for tag extraction/suggestion; or te
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FrontPage / 言語処ç†100本ノック 3 秒後㫠NLP 100 Drill Exercises ã«ç§»å‹•ã—ã¾ã™ã€‚ (移動ã—ãªã„å ´åˆã¯ã€ä¸Šã®ãƒªãƒ³ã‚¯ã‚’クリックã—ã¦ãã ã•ã„。) © Inui Laboratory 2010-2018 All rights reserved. ç ”ç©¶å®¤ç´¹ä»‹/About Us éŽåŽ»ã«åœ¨ç±ã—ãŸãƒ¡ãƒ³ãƒãƒ¼ Members ç ”ç©¶å®¤ç’°å¢ƒ Lab Facilities â†‘ç ”ç©¶ä¼š/Research Meetings æ¦‚è¦ Overview ç·åˆç ”究会 Research Seminar æ„å‘³ç ”ç©¶ä¼š SIG Semantics è«‡è©±ç ”ç©¶ä¼š SIG Discourse 知è˜ç²å¾—ç ”ç©¶ä¼š SIG Knowledge Acquisition Embeddingç ”ç©¶ä¼š SIG Embedding KIAI Knowledge-Intensive Artificial Intellige
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Dependency Tree-based Sentiment Classification using CRFs with Hidden Variables
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