Word embedding

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 many dimensions per word to a continuous vector space with a much lower dimension.

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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

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]

Limitations

One of the main limitations of word embeddings (word vector space models in general) is that words with multiple meanings are conflated into a single representation (a single vector in the semantic space). In other words, polysemy and homonymy are not handled properly. For example, in the sentence “The club I tried yesterday was great!”, it is not clear if the term club is related to the word sense of a club sandwich, baseball club, clubhouse, golf club, or any other sense that club might have. The necessity to accommodate multiple meanings per word in different vectors (multi-sense embeddings) is the motivation for several contributions in NLP to split single-sense embeddings into multi-sense ones.[17][18]

Most approaches that produce multi-sense embeddings can be divided into two main categories for their word sense representation, i.e., unsupervised and knowledge-based.[19] Based on word2vec skip-gram, Multi-Sense Skip-Gram (MSSG)[20] performs word-sense discrimination and embedding simultaneously, improving its training time, while assuming a specific number of senses for each word. In the Non-Parametric Multi-Sense Skip-Gram (NP-MSSG) this number can vary depending on each word. Combining the prior knowledge of lexical databases (e.g., WordNet, ConceptNet, BabelNet), word embeddings and word sense disambiguation, Most Suitable Sense Annotation (MSSA)[21] labels word-senses through an unsupervised and knowledge-based approach considering a word’s context in a pre-defined sliding window. Once the words are disambiguated, they can be used in a standard word embeddings technique, so multi-sense embeddings are produced. MSSA architecture allows the disambiguation and annotation process to be performed recurrently in a self-improving manner.

The use of multi-sense embeddings is known to improve performance in several NLP tasks, such as part-of-speech tagging, semantic relation identification, and semantic relatedness. However, tasks involving named entity recognition and sentiment analysis seem not to benefit from a multiple vector representation.[22]

For biological sequences: BioVectors

Word embeddings for n-grams in biological sequences (e.g. DNA, RNA, and Proteins) for bioinformatics applications have been proposed by Asgari and Mofrad.[23] 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[23] suggest that BioVectors can characterize biological sequences in terms of biochemical and biophysical interpretations of the underlying patterns.

Thought vectors

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.[24]

Software

Software for training and using word embeddings includes Tomas Mikolov's Word2vec, Stanford University's GloVe,[25] AllenNLP's Elmo,[26] fastText, Gensim,[27] Indra[28] 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.[29]

Examples of application

For instance, the fastText is also used to calculate word embeddings for text corpora in Sketch Engine that are available online.[30]

gollark: ++magic py await ctx.send("&man")return None
gollark: ++magic py await ctx.send("&ver")return None
gollark: ++magic py await ctx.send("&ver")
gollark: &ver
gollark: &about

See also

References
  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. 53: 137–165. 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.CS1 maint: ref=harv (link) 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 10.1.1.111.3313. doi:10.1126/science.290.5500.2323. PMID 11125150.
  15. word2vec
  16. "A Scalable Hierarchical Distributed Language Model". Advances in Neural Information Processing Systems 21 (NIPS 2008). Curran Associates, Inc.: 1081–1088 2009.
  17. Reisinger, Joseph; Mooney, Raymond J. (2010). Multi-Prototype Vector-Space Models of Word Meaning. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Los Angeles, California: Association for Computational Linguistics. pp. 109–117. ISBN 978-1-932432-65-7. Retrieved October 25, 2019.
  18. Huang, Eric. (2012). Improving word representations via global context and multiple word prototypes. OCLC 857900050.
  19. Camacho-Collados, Jose; Pilehvar, Mohammad Taher (2018). From Word to Sense Embeddings: A Survey on Vector Representations of Meaning. arXiv:1805.04032. Bibcode:2018arXiv180504032C.
  20. Neelakantan, Arvind; Shankar, Jeevan; Passos, Alexandre; McCallum, Andrew (2014). "Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space". Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics: 1059–1069. arXiv:1504.06654. doi:10.3115/v1/d14-1113.
  21. Ruas, Terry; Grosky, William; Aizawa, Akiko (2019-12-01). "Multi-sense embeddings through a word sense disambiguation process". Expert Systems with Applications. 136: 288–303. doi:10.1016/j.eswa.2019.06.026. hdl:2027.42/145475. ISSN 0957-4174.
  22. Li, Jiwei; Jurafsky, Dan (2015). "Do Multi-Sense Embeddings Improve Natural Language Understanding?". Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics: 1722–1732. arXiv:1506.01070. doi:10.18653/v1/d15-1200.
  23. 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.
  24. Kiros, Ryan; Zhu, Yukun; Salakhutdinov, Ruslan; Zemel, Richard S.; Torralba, Antonio; Urtasun, Raquel; Fidler, Sanja (2015). "skip-thought vectors". arXiv:1506.06726 [cs.CL].
  25. "GloVe".
  26. "Elmo".
  27. "Gensim".
  28. "Indra". 2018-10-25.
  29. Ghassemi, Mohammad; Mark, Roger; Nemati, Shamim (2015). "A Visualization of Evolving Clinical Sentiment Using Vector Representations of Clinical Notes" (PDF). Computing in Cardiology.
  30. "Embedding Viewer". Embedding Viewer. Lexical Computing. Retrieved 7 Feb 2018.
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