Word embedding

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Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers. Conceptually it involves a mathematical embedding from a space with one dimension per word to a continuous vector space with a much lower dimension.

Methods to generate this mapping include neural networks,[1] dimensionality reduction on the word co-occurrence matrix,[2][3][4] probabilistic models,[5] explainable knowledge base method,[6] and explicit representation in terms of the context in which words appear.[7]

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[8] and sentiment analysis.[9]

Development of technique[edit]

In linguistics word embeddings were discussed in the research area of distributional semantics. It aims to quantify and categorize semantic similarities between linguistic items based on their distributional properties in large samples of language data. The underlying idea that "a word is characterized by the company it keeps" was popularized by Firth.[10]

The technique of representing words as vectors has roots in the 1960s with the development of the vector space model for information retrieval. Reducing the number of dimensions using singular value decomposition then led to the introduction of latent semantic analysis in the late 1980s.[11] In 2000 Bengio et al. provided in a series of papers the "Neural probabilistic language models" to reduce the high dimensionality of words representations in contexts by "learning a distributed representation for words". (Bengio et al, 2003).[12] Word embeddings come in two different styles, one in which words are expressed as vectors of co-occurring words, and another in which words are expressed as vectors of linguistic contexts in which the words occur; these different styles are studied in (Lavelli et al, 2004).[13] Roweis and Saul published in Science how to use "locally linear embedding" (LLE) to discover representations of high dimensional data structures.[14] The area developed gradually and really took off after 2010, partly because important advances had been made since then on the quality of vectors and the training speed of the model.

There are many branches and many research groups working on word embeddings. In 2013, a team at Google led by Tomas Mikolov created word2vec, a word embedding toolkit which can train vector space models faster than the previous approaches.[15] Most new word embedding techniques rely on a neural network architecture instead of more traditional n-gram models and unsupervised learning.[16]


One of the main limitations of word embeddings (word vector space models in general) is that possible meanings of a word are conflated into a single representation (a single vector in the semantic space). Sense embeddings[17] are a solution to this problem: individual meanings of words are represented as distinct vectors in the space.

For biological sequences: BioVectors[edit]

Word embeddings for n-grams in biological sequences (e.g. DNA, RNA, and Proteins) for bioinformatics applications have been proposed by Asgari and Mofrad.[18] Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. The results presented by Asgari and Mofrad[18] suggest that BioVectors can characterize biological sequences in terms of biochemical and biophysical interpretations of the underlying patterns.

Thought vectors[edit]

Thought vectors are an extension of word embeddings to entire sentences or even documents. Some researchers hope that these can improve the quality of machine translation.[19]


Software for training and using word embeddings includes Tomas Mikolov's Word2vec, Stanford University's GloVe,[20] AllenNLP's Elmo,[21] fastText, Gensim,[22] Indra[23] and Deeplearning4j. Principal Component Analysis (PCA) and T-Distributed Stochastic Neighbour Embedding (t-SNE) are both used to reduce the dimensionality of word vector spaces and visualize word embeddings and clusters.[24]

Examples of application[edit]

For instance, the fastText is also used to calculate word embeddings for text corpora in Sketch Engine that are available online.[25]

See also[edit]


  1. ^ 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].
  2. ^ Lebret, Rémi; Collobert, Ronan (2013). "Word Emdeddings through Hellinger PCA". Conference of the European Chapter of the Association for Computational Linguistics (EACL). 2014. arXiv:1312.5542. Bibcode:2013arXiv1312.5542L.
  3. ^ Levy, Omer; Goldberg, Yoav (2014). Neural Word Embedding as Implicit Matrix Factorization (PDF). NIPS.
  4. ^ 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).
  5. ^ Globerson, Amir (2007). "Euclidean Embedding of Co-occurrence Data" (PDF). Journal of Machine Learning Research.
  6. ^ Qureshi, M. Atif; Greene, Derek (2018-06-04). "EVE: explainable vector based embedding technique using Wikipedia". Journal of Intelligent Information Systems. arXiv:1702.06891. doi:10.1007/s10844-018-0511-x. ISSN 0925-9902.
  7. ^ Levy, Omer; Goldberg, Yoav (2014). Linguistic Regularities in Sparse and Explicit Word Representations (PDF). CoNLL. pp. 171–180.
  8. ^ Socher, Richard; Bauer, John; Manning, Christopher; Ng, Andrew (2013). Parsing with compositional vector grammars (PDF). Proc. ACL Conf.
  9. ^ 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.
  10. ^ Firth, J.R. (1957). "A synopsis of linguistic theory 1930-1955". Studies in Linguistic Analysis: 1–32. Reprinted in F.R. Palmer, ed. (1968). Selected Papers of J.R. Firth 1952-1959. London: Longman.
  11. ^ Sahlgren, Magnus. "A brief history of word embeddings".
  12. ^ Bengio, Yoshua; Schwenk, Holger; Senécal, Jean-Sébastien; Morin, Fréderic; Gauvain, Jean-Luc (2006). A Neural Probabilistic Language Model. Studies in Fuzziness and Soft Computing. 194. pp. 137–186. doi:10.1007/3-540-33486-6_6. ISBN 978-3-540-30609-2.
  13. ^ Lavelli, Alberto; Sebastiani, Fabrizio; Zanoli, Roberto (2004). Distributional term representations: an experimental comparison. 13th ACM International Conference on Information and Knowledge Management. pp. 615–624. doi:10.1145/1031171.1031284.
  14. ^ Roweis, Sam T.; Saul, Lawrence K. (2000). "Nonlinear Dimensionality Reduction by Locally Linear Embedding". Science. 290 (5500): 2323–6. Bibcode:2000Sci...290.2323R. CiteSeerX doi:10.1126/science.290.5500.2323. PMID 11125150.
  15. ^ word2vec
  16. ^ "A Scalable Hierarchical Distributed Language Model". Curran Associates, Inc. 2009: 1081–1088.
  17. ^ Camacho-Collados, Jose; Pilehvar, Mohammad Taher (2018). From Word to Sense Embeddings: A Survey on Vector Representations of Meaning. arXiv:1805.04032. Bibcode:2018arXiv180504032C.
  18. ^ a b Asgari, Ehsaneddin; Mofrad, Mohammad R.K. (2015). "Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics". PLOS ONE. 10 (11): e0141287. arXiv:1503.05140. Bibcode:2015PLoSO..1041287A. doi:10.1371/journal.pone.0141287. PMC 4640716. PMID 26555596.
  19. ^ Kiros, Ryan; Zhu, Yukun; Salakhutdinov, Ruslan; Zemel, Richard S.; Torralba, Antonio; Urtasun, Raquel; Fidler, Sanja (2015). "skip-thought vectors". arXiv:1506.06726 [cs.CL].
  20. ^ "GloVe".
  21. ^ "Elmo".
  22. ^ "Gensim".
  23. ^ "Indra". 2018-10-25.
  24. ^ Ghassemi, Mohammad; Mark, Roger; Nemati, Shamim (2015). "A Visualization of Evolving Clinical Sentiment Using Vector Representations of Clinical Notes" (PDF). Computing in Cardiology.
  25. ^ "Embedding Viewer". Embedding Viewer. Lexical Computing. Retrieved 7 Feb 2018.