See also GloVe, wiki2vec, WordRank, sense2vec. A Continuous embedding method.
Table of Contents
Software and Libraries #
Intro and Theory #
- A Neural Probabilistic Language Model
- Distributed Representations of Words and Phrases and their Compositionality
It was recently shown that the word vectors capture many linguistic regularities, for example vector operations vector('Paris') - vector('France') + vector('Italy') results in a vector that is very close to vector('Rome'), and vector('king') - vector('man') + vector('woman') is close to vector('queen') [3, 1].
- Paper/Levy2014: Neural World Embedding as Implicit Matrix Factorization
- Paper/Levy2014a: Linguistic Regularities in Sparse and Explicit Word Representations
- word2vec Parameter Learning Explained
Methods #
Tutorials #
- http://rare-technologies.com/word2vec-tutorial/
- Deep Learning Basics: Neural Networks, Backpropagation and Stochastic Gradient Descent
- 한국어와 NLTK, Gensim의 만남
- Demystifying Word2Vec
- Jurafsky and Martin: Vector Semantics, Part II
- Chris McCormick Word2Vec Tutorial - The Skip-Gram Model
- TensorFlow Vector Representations of Words
Articles #
- http://deeplearning4j.org/word2vec.html#crazy
-
what does the output vector of a word in word2vec represent?
Presentations #
- Machine Perception with Neural Networks by Ilya Sutskever
- Text By the Bay 2015: Chris Moody, A Word is Worth a Thousand Vectors
Topics #
Paragaraph vectors #
Comparing multiple sets of vectors #
Optimal dimension #
Sentiment analysis #
Application to networks #
Scalability #
Gender bias #
Clinical concepts #
Incoming Links #
Related Articles (Article 0) #
- Continuous embedding
- Deep learning
- Gender bias
- Gensim
- Hierarchical softmax
- Neural network
- Paper/Perozzi2014
- Sentiment analysis
- Skip-gram
- Tomas Mikolov
- Word embedding
- WordRank
- sense2vec
- wiki2vec
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