Word embedding

Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing where words or phrases from the vocabulary are mapped to vectors of real numbers in a low-dimensional space relative to the vocabulary size ("continuous space").

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

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[7] and sentiment analysis.[8]

Word Embedding 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.[9] 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[9] suggest that BioVectors can characterize biological sequences in terms of biochemical and biophysical interpretations of the underlying patterns.

Software

Software for training and using word embeddings includes Google's Word2vec, Stanford University's GloVe[10] and Deeplearning4j. Principal Component Analysis (PCA) and T-Distributed Stochastic Neighbor Embedding (t-SNE) are both used to reduce the dimensionality of word vector spaces and visualize word embeddings and clusters.[11]

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. ↑ Barkan, Oren (2015). "Bayesian Neural Word Embedding". arXiv:1603.06571 [cs.CL].
  3. ↑ Lebret, Rémi; Collobert, Ronan (2013). "Word Emdeddings through Hellinger PCA". arXiv:1312.5542 [cs.CL].
  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. ↑ Levy, Omer; Goldberg, Yoav (2014). Linguistic Regularities in Sparse and Explicit Word Representations (PDF). CoNLL. pp. 171–180.
  7. ↑ Socher, Richard; Bauer, John; Manning, Christopher; Ng, Andrew (2013). Parsing with compositional vector grammars (PDF). Proc. ACL Conf.
  8. ↑ 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.
  9. 1 2 Asgari, Ehsaneddin; Mofrad, Mohammad R.K. (2015). "Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics". PloS one 10 (11): e0141287.
  10. ↑ "GloVe".
  11. ↑ Ghassemi, Mohammad; Mark, Roger; Nemati, Shamim (2015). "A Visualization of Evolving Clinical Sentiment Using Vector Representations of Clinical Notes" (PDF). Computing in Cardiology.
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