"Word2vec"의 두 판 사이의 차이
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imported>Pythagoras0 |
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* [[Singular value decomposition]] | * [[Singular value decomposition]] | ||
* [[FastText]] | * [[FastText]] | ||
| − | + | * [[나무위키 코퍼스]] | |
==computational resource== | ==computational resource== | ||
2018년 5월 28일 (월) 02:25 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.
pretrained korean word2vec
memo