"Word2vec"의 두 판 사이의 차이
둘러보기로 가기
검색하러 가기
imported>Pythagoras0 |
imported>Pythagoras0 |
||
| 4번째 줄: | 4번째 줄: | ||
** 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. | ** 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== | ||
| + | * https://github.com/Kyubyong/wordvectors | ||
| + | |||
2018년 5월 8일 (화) 00:24 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