"Word2vec"의 두 판 사이의 차이
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imported>Pythagoras0 |
imported>Pythagoras0 |
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| 3번째 줄: | 3번째 줄: | ||
* document similarity | * document similarity | ||
** https://rare-technologies.com/performance-shootout-of-nearest-neighbours-contestants/ | ** 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. | ||
2017년 5월 2일 (화) 07:59 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