Search results with tag "Distributed representations"
GloVe: Global Vectors for Word ... - Stanford University
nlp.stanford.edudistributed representations (Bengio, 2009). The two main model families for learning word vectors are: 1) global matrix factorization meth-ods, such as latent semantic analysis (LSA) (Deer-wester et al., 1990) and 2) local context window methods, such as the skip-gram model of Mikolov et al. (2013c). Currently, both families suffer sig-
![arXiv:1301.3781v3 [cs.CL] 7 Sep 2013](/cache/preview/4/d/5/0/4/3/4/0/thumb-4d504340120163c0bdf3f4678d8d217f.jpg)
arXiv:1301.3781v3 [cs.CL] 7 Sep 2013
arxiv.orgthe most successful concept is to use distributed representations of words [10]. For example, neural network based language models significantly outperform N-gram models [1, 27, 17]. 1.1 Goals of the Paper The main goal of this paper is to introduce techniques that can be used for learning high-quality word
Distributed Representations of Words and Phrases and their ...
papers.nips.ccDistributed representations of words in a vector space help learning algorithms to achieve better performancein natural language processing tasks by groupingsimilar words. One of the earliest use of word representations dates back to 1986 due …
Distributed Representations of Sentences and Documents
cs.stanford.edumatrix-vector operations (Socher et al., 2011b). Both ap-proaches have weaknesses. The first approach, weighted averaging of word vectors, loses the word order in the same way as the standard bag-of-words models do. The second approach, using a parse tree to combine word vectors, has been shown to work for only sentences because it relies on ...