"Word2vec"의 두 판 사이의 차이
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imported>Pythagoras0 |
imported>Pythagoras0 |
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| 16번째 줄: | 16번째 줄: | ||
==computational resource== | ==computational resource== | ||
* https://drive.google.com/file/d/0B8XXo8Tve1cxWTVTXzdNTlV4ek0/view | * https://drive.google.com/file/d/0B8XXo8Tve1cxWTVTXzdNTlV4ek0/view | ||
| + | * https://fasttext.cc/docs/en/pretrained-vectors.html#content | ||
[[분류:계산]] | [[분류:계산]] | ||
2017년 8월 2일 (수) 20:29 판
gensim
- https://rare-technologies.com/word2vec-tutorial/
- document similarity
- https://rare-technologies.com/performance-shootout-of-nearest-neighbours-contestants/
- Using gensim’s memory-friendly streaming API I then converted these plain text tokens to TF-IDF vectors, ran Singular Value Decomposition (SVD) on this TF-IDF matrix to build a latent semantic analysis (LSA) model and finally stored each Wikipedia document as a 500-dimensional LSA vector to disk.
memo