메뉴 건너뛰기




Volumn 31, Issue 6, 2016, Pages 5-14

How to generate a good word embedding

Author keywords

distributed representation; intelligent systems; neural network; word embedding

Indexed keywords

INTELLIGENT SYSTEMS; NEURAL NETWORKS;

EID: 85012088618     PISSN: 15411672     EISSN: None     Source Type: Journal    
DOI: 10.1109/MIS.2016.45     Document Type: Article
Times cited : (322)

References (22)
  • 1
    • 80053495924 scopus 로고    scopus 로고
    • Word representations: A simple and general method for semi-supervised learning
    • J. Turian, L. Ratinov, and Y. Bengio, "Word Representations: A Simple and General Method for Semi-supervised Learning," Proc. Assoc. Computational Linguistics, 2010, pp. 384-394.
    • (2010) Proc. Assoc. Computational Linguistics , pp. 384-394
    • Turian, J.1    Ratinov, L.2    Bengio, Y.3
  • 2
    • 84906930943 scopus 로고    scopus 로고
    • Don't Count, Predict! A Systematic Comparison of Context-Counting vs. Context-Predicting Semantic Vectors
    • M. Baroni, G. Dinu, and G. Kruszewski, "Don't Count, Predict! A Systematic Comparison of Context-Counting vs. Context-Predicting Semantic Vectors," Proc. Assoc. Computational Linguistics, 2014, pp. 238-247.
    • (2014) Proc. Assoc. Computational Linguistics , pp. 238-247
    • Baroni, M.1    Dinu, G.2    Kruszewski, G.3
  • 4
    • 85083951332 scopus 로고    scopus 로고
    • Efficient estimation of word representations in vector space
    • T. Mikolov et al., "Efficient Estimation of Word Representations in Vector Space," Proc. Int'l Conf. Learning Representation, 2013; http://arxiv.org/pdf/1301.3781.pdf.
    • (2013) Proc. Int'l Conf. Learning Representation
    • Mikolov, T.1
  • 5
    • 0142166851 scopus 로고    scopus 로고
    • A neural probabilistic language model
    • Y. Bengio et al., "A Neural Probabilistic Language Model," J. Machine Learning Research, vol. 3, 2003, pp. 1137-1155.
    • (2003) J. Machine Learning Research , vol.3 , pp. 1137-1155
    • Bengio, Y.1
  • 6
    • 56449095373 scopus 로고    scopus 로고
    • A unified architecture for natural language processing: Deep neural networks with multitask learning
    • R. Collobert and J. Weston, "A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning," Proc. Int'l Conf. Machine Learning, 2008, pp. 160-167.
    • (2008) Proc. Int'l Conf. Machine Learning , pp. 160-167
    • Collobert, R.1    Weston, J.2
  • 7
    • 80053495924 scopus 로고    scopus 로고
    • Word representations: A simple and general method for semi-supervised learning
    • J. Turian, L. Ratinov, and Y. Bengio, "Word Representations: A Simple and General Method for Semi-supervised Learning," Proc. Assoc. Computational Linguistics, 2010, pp. 384-394.
    • (2010) Proc. Assoc. Computational Linguistics , pp. 384-394
    • Turian, J.1    Ratinov, L.2    Bengio, Y.3
  • 8
    • 85083951332 scopus 로고    scopus 로고
    • Efficient estimation of word representations in vector space
    • arXiv: 1301.3781
    • T. Mikolov et al., "Efficient Estimation of Word Representations in Vector Space," Proc. Int'l Conf. Learning Representation, 2013, arXiv:1301.3781.
    • (2013) Proc. Int'l Conf. Learning Representation
    • Mikolov, T.1
  • 9
    • 34547970628 scopus 로고    scopus 로고
    • Three new graphical models for statistical language modelling
    • A. Mnih and G. Hinton, "Three New Graphical Models for Statistical Language Modelling," Proc. Int'l Conf. Machine Learning, 2007, pp. 641-648.
    • (2007) Proc. Int'l Conf. Machine Learning , pp. 641-648
    • Mnih, A.1    Hinton, G.2
  • 10
    • 0000679216 scopus 로고
    • Distributional structure
    • Z.S. Harris, "Distributional Structure," Word, vol. 10, no. 2, 1954, pp. 146-162.
    • (1954) Word , vol.10 , Issue.2 , pp. 146-162
    • Harris, Z.S.1
  • 11
    • 35448938531 scopus 로고    scopus 로고
    • On the computational basis of learning and cognition: Arguments from LSA
    • T.K. Landauer, "On the Computational Basis of Learning and Cognition: Arguments from LSA," Psychology of Learning and Motivation, vol. 41, 2002, pp. 43-84.
    • (2002) Psychology of Learning and Motivation , vol.41 , pp. 43-84
    • Landauer, T.K.1
  • 12
    • 84906930943 scopus 로고    scopus 로고
    • Don't count, predict! A systematic comparison of context-counting vs. Context-predicting semantic vectors
    • M. Baroni, G. Dinu, and G. Kruszewski, "Don't Count, Predict! A Systematic Comparison of Context-Counting vs. Context-Predicting Semantic Vectors," Proc. Assoc. Computational Linguistics, 2014, pp. 238-247.
    • (2014) Proc. Assoc. Computational Linguistics , pp. 238-247
    • Baroni, M.1    Dinu, G.2    Kruszewski, G.3
  • 14
    • 0344029639 scopus 로고    scopus 로고
    • Placing search in context: The concept revisited
    • L. Finkelstein et al., "Placing Search in Context: The Concept Revisited," ACM Trans. Information Systems, vol. 20, no. 1, 2002, pp. 116-131.
    • (2002) ACM Trans. Information Systems , vol.20 , Issue.1 , pp. 116-131
    • Finkelstein, L.1
  • 15
    • 0000600219 scopus 로고    scopus 로고
    • A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge
    • T.K. Landauer and S.T. Dumais, "A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge," Psychological Rev., vol. 104, no. 2, 1997, p. 211.
    • (1997) Psychological Rev. , vol.104 , Issue.2 , pp. 211
    • Landauer, T.K.1    Dumais, S.T.2
  • 16
    • 84859023447 scopus 로고    scopus 로고
    • Learning word vectors for sentiment analysis
    • A.L. Maas et al., "Learning Word Vectors for Sentiment Analysis," Proc. Assoc. Computational Linguistics, 2011, pp. 142-150.
    • (2011) Proc. Assoc. Computational Linguistics , pp. 142-150
    • Maas, A.L.1
  • 17
    • 77949522811 scopus 로고    scopus 로고
    • Why does unsupervised pre-training help deep learning?
    • D. Erhan et al., "Why Does Unsupervised Pre-training Help Deep Learning?," J. Machine Learning Research, vol. 11, 2010, pp. 625-660.
    • (2010) J. Machine Learning Research , vol.11 , pp. 625-660
    • Erhan, D.1
  • 18
  • 19
    • 84926358845 scopus 로고    scopus 로고
    • Recursive deep models for semantic compositionality over a sentiment treebank
    • R. Socher et al., "Recursive Deep Models for Semantic Compositionality over a Sentiment Treebank," Proc. Empirical Methods in Natural Language Processing, 2013, pp. 1631-1642.
    • (2013) Proc. Empirical Methods in Natural Language Processing , pp. 1631-1642
    • Socher, R.1
  • 20
    • 80053558787 scopus 로고    scopus 로고
    • Natural language processing (almost) from scratch
    • R. Collobert et al., "Natural Language Processing (almost) from Scratch," J. Machine Learning Research, vol. 12, no. 8, 2011, pp. 2493-2537.
    • (2011) J. Machine Learning Research , vol.12 , Issue.8 , pp. 2493-2537
    • Collobert, R.1
  • 22
    • 84898956512 scopus 로고    scopus 로고
    • Distributed representations of words and phrases and their compositionality
    • T. Mikolov et al., "Distributed Representations of Words and Phrases and Their Compositionality," Proc. Neural Information Processing Systems, 2013, pp. 3111-3119.
    • (2013) Proc. Neural Information Processing Systems , pp. 3111-3119
    • Mikolov, T.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.