Linguistic Regularities In Continuous Space Word Representations Filetype:pdf

Learning continuous space models 10 •How do we learn the word representations z for each word in the vocabulary? •How do we learn the model that predicts a word or its representation ẑ t given a word context? •Simultaneous learning of model and representation

Using Word2Vec to Process Big Text Data. Representations of Words and Phrases and their Compositionality,”. G. Zweig, “Linguistic Regularities in Continuous Space Word Representations.

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Geoffrey Zweig. Linguistic regularities in continuous space word representations. InProceedings of the 2013 Conference of the North American Chapter of the Asso-ciation for Computational Linguistics: Human Language Technologies, pages 746 751, 2013. [Penningtonet al., 2014] Jeffrey Pennington, Richard Socher, and Christopher Manning. Glove.

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Lecture 6: Vector Space Model Kai-Wei Chang CS @ University of Virginia [email protected] Linguistic Regularities in Sparse and Explicit Word Representations, Levy Goldberg, CoNLL 14. Continuous representations for entities 6501 Natural Language Processing 47

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Learning Distributed Word Representations and Applications in Biomedial Natural Language Processing Jiaping Zhenga, Hong Yub a University of Massachusetts Amherst b University of Massachusetts Medical School Abstract A common challenge for biomedical natural language

one dimension is on, word embedding preserves rich linguistic regularities of words with each di-mension hopefully representing a latent feature. Similar words are expected to be distributed close to one another in the embedding space. Conse-quently, word embeddings can be benecial for a variety of NLP applications in different ways,

Learning continuous space models 10 •How do we learn the word representations z for each word in the vocabulary? •How do we learn the model that predicts a word or its representation ẑ t given a word context? •Simultaneous learning of model and representation

• Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig, Linguistic Regularities in Continuous Space Word Representations, in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT-2013), Association for Computational Linguistics, 27 May 2013.

[2] Tomas Mikolov, Wen-tau Yih and Geoffrey Zweig. Linguistic Regularities in Continuous Space Word Representations. In Proceedings of NAACL HLT, 2013. [3] C.J. Bezdek, P attern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981 [4] Zewei Chu, Hai Wang, Kevin Gimpel, and David McAllester.

[2] Linguistic Regularities in Continuous Space Word Representations., 2013. [3] The Stanford CoreNLP Natural Language Processing Toolkit, 2014. [4] Association for Computational Linguistics. Identifying relations for open information extraction, 2011. [5] J. Fan, A. Kalyanpur, D. C. Gondek, and D. A. Ferrucci. Automatic knowledge extraction from

2013. Linguistic regularities in continuous space word representations. In HLT-NAACL , volume 13, pages 746 751. Carina Silberer and Mirella Lapata. 2014. Learn-ing grounded meaning representations with autoen-coders. In ACL (1) , pages 721 732. EkaterinaVylomova, LauraRimmel, TrevorCohn, and Timothy Baldwin. 2015. Take and took, gaggle and

monolingual distributed representations into a lan-guage independent space (i.e. bilingual or multilin-gual word embeddings) by jointly training on pair of languages. Although the overall goal of these approaches is to capture linguistic regularities in words that share same semantic and syntactic space across languages, they differ in their.

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Learning continuous space models 10 •How do we learn the word representations z for each word in the vocabulary? •How do we learn the model that predicts. have linguistic regularities “a” is to “b” as “c” is to _ Syntactic: king is to kings as queen is to queens

ContinuousDistributedRepresentationsofWordsasInputofLSTMLM 154 short-listed to 10k most frequent words, all numbers were unified into N tag and a

• Mikolov et al. 2013a, b. c. Efficient Estimation of Word Representations in Vector Space. Distributed Representation of Words and Phrases and their Compositionality. Linguistic regularities in continuous space word representations. • Kim, 2014. Convolutional Neural Network for Sentence Classification. • Wu et al. 2016.

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1/16 Word Embeddings Linguistic Regularities in Continuous-Space Recurrent Neural Network Language Models- (Mikolov et al., NAACL-HLT 2013) GloVe: Global Vectors for Word Representation- (Pennington et al., EMNLP 2014) Recommended:E cient estimation of word representations in vector space- (Mikolov et al., 2014)

In this work, we only require representations for monolingual phrases that are relatively short. 1 We therefore decided to use off-the-shelf word repre-sentations to build phrase vectors. In particular, we chose the continuous bag-of-words model (Mikolov et al., 2013b) which is very fast to train and scales very well to large monolingual corpora.

Mikolov, T., Yih, W., & Zweig, G. (2013). Linguistic Regularities in Continuous Space Word Representations. Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 746-751). Atlanta, Georgia: Association for Computational Linguistics.