Word embedding

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In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning.[1] Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to vectors of real numbers.

Methods to generate this mapping include neural networks,[2] dimensionality reduction on the word co-occurrence matrix,[3][4][5] probabilistic models,[6] explainable knowledge base method,[7] and explicit representation in terms of the context in which words appear.[8]

Word and phrase embeddings, when used as the underlying input representation, have been shown to boost the performance in NLP tasks such as syntactic parsing[9] and sentiment analysis.[10]

  1. ^ Jurafsky, Daniel; H. James, Martin (2000). Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition. Upper Saddle River, N.J.: Prentice Hall. ISBN 978-0-13-095069-7.
  2. ^ Mikolov, Tomas; Sutskever, Ilya; Chen, Kai; Corrado, Greg; Dean, Jeffrey (2013). "Distributed Representations of Words and Phrases and their Compositionality". arXiv:1310.4546 [cs.CL].
  3. ^ Lebret, Rémi; Collobert, Ronan (2013). "Word Emdeddings through Hellinger PCA". Conference of the European Chapter of the Association for Computational Linguistics (EACL). Vol. 2014. arXiv:1312.5542.
  4. ^ Levy, Omer; Goldberg, Yoav (2014). Neural Word Embedding as Implicit Matrix Factorization (PDF). NIPS.
  5. ^ Li, Yitan; Xu, Linli (2015). Word Embedding Revisited: A New Representation Learning and Explicit Matrix Factorization Perspective (PDF). Int'l J. Conf. on Artificial Intelligence (IJCAI).
  6. ^ Globerson, Amir (2007). "Euclidean Embedding of Co-occurrence Data" (PDF). Journal of Machine Learning Research.
  7. ^ Qureshi, M. Atif; Greene, Derek (2018-06-04). "EVE: explainable vector based embedding technique using Wikipedia". Journal of Intelligent Information Systems. 53: 137–165. arXiv:1702.06891. doi:10.1007/s10844-018-0511-x. ISSN 0925-9902. S2CID 10656055.
  8. ^ Levy, Omer; Goldberg, Yoav (2014). Linguistic Regularities in Sparse and Explicit Word Representations (PDF). CoNLL. pp. 171–180.
  9. ^ Socher, Richard; Bauer, John; Manning, Christopher; Ng, Andrew (2013). Parsing with compositional vector grammars (PDF). Proc. ACL Conf. Archived from the original (PDF) on 2016-08-11. Retrieved 2014-08-14.
  10. ^ Socher, Richard; Perelygin, Alex; Wu, Jean; Chuang, Jason; Manning, Chris; Ng, Andrew; Potts, Chris (2013). Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank (PDF). EMNLP.