文献紹介:Revisiting Word Embedding for Contrasting Meaning

E777980f2d60fdf6b670079cf4f9072e?s=47 Van Hai
March 03, 2016
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文献紹介:Revisiting Word Embedding for Contrasting Meaning

E777980f2d60fdf6b670079cf4f9072e?s=128

Van Hai

March 03, 2016
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  1. 1 文献紹介 (2016.03.03) 長岡技術科学大学  自然言語処理    Nguyen Van Hai Revisiting Word

    Embedding for Contrasting Meaning Zhigang Chen, Wei Lin, Qian Chen, Ziaoping Chen, Si Wei, Hui Jiang and Xiaodan Zhu Proceedings of the 53th Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural language processing, p106-115
  2. 2 Introduction • Contrasting meaning is a basic aspect of

    semantics • This embedding models achieve 92% F-score on the GRE dataset, which is “most contrasting word” questions (Mohammad et al.,2008) • Direction for this basic semantics modeling problem.
  3. 3 Related work • The terms contrasting, opposite, and antonym

    have different definitions in the literature. – Contrasting word pairs having some non-zero degree of binary incompatibility • Word Embedding • Modeling Contrasting Meaning – PILSA (Yih et al., 2012) get a further improvement on the GRE benchmark, where an 81% of F-score – Bayesian probabilistic tensor factorization (Zhang et al., 2014)
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  5. 5 Top Hidden Layer(s) • It is widely recognized that

    contrasting words – e.g., good and bad • Intend to appear in similar context or co-occur with each other.
  6. 6 Stochastic Contrast Embedding (SCE) • Hinton and Roweis (2002)

    proposed a stochastic neighbor embedding (SNE) framework. • Use the concept of “neighbors” to encode the contrast pairs • Three respects are different from SNE – “neighbors” here are actually contrasting pairs – Train SCE using only lexical resources – Semantic closeness or contrast are not independent
  7. 7 Marginal Contrast Embedding (MCE) • This paper used another

    training criteria, motivated by pairwise ranking approach (Cohen et al., 1998) • This motivation – To explicitly enforce the distances between contrasting pairs to be larger than distances between unrelated word pairs. – Enforce the distances between semantically close pairs by another margin
  8. 8 Corpus Relatedness Modeling (CRM) • Mohammad et al., (2013)

    proposed a degree of contrast hypothesis: – “if a pair of words, A and B, are contrasting, then their degree of contrast is proportional to their tendency to co-occur in a large corpus.” • Use the skip-gram model (Mikolov et al., 2013) to learn the relatedness embedding
  9. 9 Semantic Differential Reconstruction (SDR) • Using factor analysis, Osgood

    et al.,(1957) identified three dimensions of semantic that account for most of the variation in the connotative meaning of adjective. • Three dimensions – Evaluative: good-bad – Potency: strong-weak – Activity: active-passive
  10. 10 Experiment Set-Up • Data – Used the “most contrasting

    word” questions collected by Mohammad et al., (2008) • Lexical Resources – WordNet (Miller, 1995) version 3.0 – Roget's Thesaurus (Kipfer, 2009) • Google Billion-Word Corpus • Evaluation Metric
  11. 11 Experiment Results • The result is presented in Table

    1 and the F-score on test set is 91% • The pie graph in Figure 2 shows the percentages of target-gold-answer word pairs • Figure 3 draws the envelope of histogram of cosine distance between all target-choice • The performances of different models are show in Figure 4
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