Sentence2Vec : Evaluation of popular theories — Part I (Simple average of word vectors)
In this series, I will be creating prototypes for generating sentence vectors using popular theories and in this particular post I will be starting with simple averaging of word vectors method for obtaining sentence vector. Edited 3rd June 2017: I am adding results of evaluations of each approach on quora duplicate question answer dataset. It has about 400,000 pairs of questions with manual evalua
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以å‰ã«ã‚‚ã¡ã‚‡ã“ã£ã¨æ›¸ã„ãŸã®ã ã‘ã©ã€è‡ªç„¶è¨€èªžã¯ãŸã„ã¦ã„次元(å˜èªžæ•°ã€æ–‡å—æ•°ãªã©)ãŒåˆã‚ãªã„。 データA　1 2 データB　1 2 3 ã“ã‚“ãªæ„Ÿã˜ã§æ¬¡å…ƒãŒåˆã£ã¦ã„ãªã„データをミニãƒãƒƒãƒã§å¦ç¿’ã—ãŸã„。 末尾をEOSã§åŸ‹ã‚ã¦ãƒŸãƒ‹ãƒãƒƒãƒ å¯å¤‰é•·ã®ãƒŸãƒ‹ãƒãƒƒãƒã®æ™‚ã«æœ«å°¾ã‚’EOSã§åŸ‹ã‚ã¦ãã®ã¾ã¾å¦ç¿’ã™ã‚‹ã¨ã¾ãšã„ã®ã§whereを使ã†ã¨ã„ã„らã—ã„。以å‰ã‹ã‚‰æŒ‘戦ã—ã¦ã„ãŸã®ã ã‘ã‚Œã©ã‚‚ãªã‹ãªã‹ã†ã¾ãã„ã‹ãšã€å…ˆæ—¥ã‚ˆã†ã‚„ãä¸ã®äººã«twitterã§æ•™ãˆã¦ã‚‚らã£ã¦å‡ºæ¥ã¾ã—ãŸã€‚ ã¾ãšæœ«å°¾ã‚’0ã‚„-1ãªã©ç‰¹æ®Šãªã‚‚ã®ã§åŸ‹ã‚ã¦æ¬¡å…ƒã‚’固定ã™ã‚‹ã€‚ä»®ã«0ã‚’EOSã«è©²å½“ã™ã‚‹IDã ã¨ã—ãŸã‚‰ã“ã®ã‚ˆã†ã«åŸ‹ã‚られる。ã“ã‚Œã§Aã‚‚Bã‚‚4次元ã§æƒã£ãŸã€‚ データA　1 2 0 0 データB　1 2 3 0 転置(numpyã®.T)ã—ãŸã‚‰ãã®ã¾ã¾å…¥åŠ›ã«ä½¿ãˆã‚‹å½¢å¼ã«ãªã£ã¦ãれる。 A B 1 1 2 2 0 3 0 0 実際ã®ã‚³ãƒ¼ãƒ‰ã ã¨ã“ã†ã€‚ a = nump
Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models · Explosion
Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. The new approach can be summarised as a simple four-step formula: embed, encode, attend, predict. This post explains the components of this new approach, and shows how they’re put together i
GitHub - julius-speech/julius: Open-Source Large Vocabulary Continuous Speech Recognition Engine
"Julius" is a high-performance, small-footprint large vocabulary continuous speech recognition (LVCSR) decoder software for speech-related researchers and developers. Based on word N-gram and context-dependent HMM, it can perform real-time decoding on various computers and devices from micro-computer to cloud server. The algorithm is based on 2-pass tree-trellis search, which fully incorporates ma
Pythonã§Bag of Wordsã¨SVMを使ã£ãŸã‚¿ã‚¤ãƒˆãƒ«ã®ã‚«ãƒ†ã‚´ãƒªåˆ†é¡ž - stMind
cc licensed ( BY ) flickr photo shared by Loco Steve 週末ã«è©¦ãã†ã®ã‚³ãƒ¼ãƒŠãƒ¼ã€‚ ã¡ã‚‡ã†ã©è‰¯ã„ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ãŒã‚ã£ãŸã®ã§ã€ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã‚’用æ„ã—ã¦ã‚„ã£ã¦ã¿ã¾ã—ãŸã€‚ å•é¡Œ How can I get a computer to tell me what an article is about (provided methods such as bribery and asking politely do not work)? ã‚る記事ãŒä½•ã«ã¤ã„ã¦æ›¸ã‹ã‚Œã¦ã„ã‚‹ã®ã‹ã€ã‚³ãƒ³ãƒ”ュータã«ç†è§£ã•ã›ã‚‹ã«ã¯ã©ã†ã™ã‚Œã°è‰¯ã„ã‹? ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã§ã¯æ‰‹å‹•ã§ä½œã£ãŸãƒ‡ãƒ¼ã‚¿ã‚’使ã£ã¦çŠ¬ã‚‚ã—ãã¯ã‚µãƒ³ãƒ‰ã‚¤ãƒƒãƒã®2クラス分類をã—ã¦ã„ã¾ã™ã€‚ ã“ã“ã§ã¯ã€Google Newsã§iPadã®ãƒ‹ãƒ¥ãƒ¼ã‚¹ã€ã‚½ãƒäº”輪ã®ãƒ‹ãƒ¥ãƒ¼ã‚¹ã¨ã‚«ãƒ†ã‚´ãƒªåˆ†ã‘ã•ã‚Œã¦ã„る記事ã®ã‚¿ã‚¤ãƒˆãƒ«ã‚’使ã£ã¦ã€ ã‚るタイトルãŒiPa
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