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How to marry a star: Probabilistic constraints for meaning in context
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In: Proceedings of the Society for Computation in Linguistics (2021)
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How to marry a star: probabilistic constraints for meaning in context ...
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Exploring distributional semantics in lexical representations and narrative modeling
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Testing the usefulness of RST and more general representations for discourse analysis across domains and applications
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Analyzing group behavior from language use with natural language processing and experimental methods : three applications in political science and sociology
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Expressively vulgar : the socio-dynamics and intentionality of vulgarity and its effects on sentiment analysis in social media
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Identifying lexical relationships and entailments with distributional semantics
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Applying corpus and computational methods to loanword research : new approaches to Anglicisms in Spanish
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Distributional model on a diet : one-shot word learning from text only
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Variation in English world-wide : varieties and genres in a quantitative perspective
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Word Sense Clustering and Clusterability
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In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01838502 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2016, 42, pp.245-275. ⟨10.1162/COLI⟩ (2016)
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Graph-based translation via graph segmentation
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In: Li, Liangyou orcid:0000-0002-0279-003X , Way, Andy orcid:0000-0001-5736-5930 and Liu, Qun orcid:0000-0002-7000-1792 (2016) Graph-based translation via graph segmentation. In: 54th Annual Meeting of the Association for Computational Linguistics, 7-12 Aug 2016, Berlin, Germany. (2016)
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Phrase-level combination of SMT and TM using constrained word lattice
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In: Li, Liangyou orcid:0000-0002-0279-003X , Way, Andy orcid:0000-0001-5736-5930 and Liu, Qun orcid:0000-0002-7000-1792 (2016) Phrase-level combination of SMT and TM using constrained word lattice. In: 54th Annual Meeting of the Association for Computational Linguistics, 7-11 Aug 2016, Berlin, Germany. (2016)
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Relations such as Hypernymy: Identifying and Exploiting Hearst Patterns in Distributional Vectors for Lexical Entailment ...
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Abstract:
We consider the task of predicting lexical entailment using distributional vectors. We perform a novel qualitative analysis of one existing model which was previously shown to only measure the prototypicality of word pairs. We find that the model strongly learns to identify hypernyms using Hearst patterns, which are well known to be predictive of lexical relations. We present a novel model which exploits this behavior as a method of feature extraction in an iterative procedure similar to Principal Component Analysis. Our model combines the extracted features with the strengths of other proposed models in the literature, and matches or outperforms prior work on multiple data sets. ... : EMNLP 2016 ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1605.05433 https://arxiv.org/abs/1605.05433
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