word2vec

Transcript

1. Word2vec Word representation in Vector Space Javier Honduvilla Coto
2. What’s word2vec? • Vector representation of words • Uses neural networks (more on the training later) • Unsupervised • Published in 2013 by Google researchers and engineers • A companion C implementation was published with the paper
3. Why? Image and video representation is pretty rich, usually done with humongous vectors – commonly having a high dimensionality. Meanwhile, words are usually mapped to arbitrary IDs such as the word itself.
4. Previous work • Counting based methods: probability of a word happening with some neighbour words • Predictive models: guess using nearby words’ vectors
5. Cool things of this model • Continuous Bag of Words: predict a word using previous words (good in small models) • Skip-Gram: predict words which are close, from the context from an input word (good for big models) => • Pretty good performance (100 billions words/day in a single box) • 33 billions: 72% accuracy
7. Example (distance to Sweden)
9. Vector operations!!
12. Appendix • Original paper: http://papers.nips.cc/paper/5021-distributed-representations-of-words- and-phrases-and-their-compositionality.pdf • Original implementation: https://code.google.com/archive/p/word2vec • Interesting JVM implementation https://deeplearning4j.org/word2vec