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Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity
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In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02975786 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2020, 46 (4), pp.847-897 ; https://direct.mit.edu/coli/article/46/4/847/97326/Multi-SimLex-A-Large-Scale-Evaluation-of (2020)
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Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
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Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02425462 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2019, 45 (3), pp.559-601. ⟨10.1162/coli_a_00357⟩ ; https://www.mitpressjournals.org/doi/abs/10.1162/coli_a_00357 (2019)
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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In: Computational Linguistics, Vol 45, Iss 3, Pp 559-601 (2019) (2019)
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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In: https://hal.archives-ouvertes.fr/hal-01856176 ; 2018 (2018)
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
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Cognitive Aspects of Computational Language Acquisition
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In: https://hal.archives-ouvertes.fr/hal-00783282 ; Springer, pp.330, 2013, 978-3-642-31863-4 (2013)
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A Tensor-based Factorization Model of Semantic Compositionality
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In: Proceedings of the Conference of the North American Chapter of the Association of Computational Linguistics (HTL-NAACL) ; Conference of the North American Chapter of the Association of Computational Linguistics (HTL-NAACL) ; https://hal.archives-ouvertes.fr/hal-00997334 ; Conference of the North American Chapter of the Association of Computational Linguistics (HTL-NAACL), Jun 2013, Atlanta, United States. pp.1142-1151 (2013)
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Computational Modeling as a Methodology for Studying Human Language Learning
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In: Cognitive Aspects of Computational Language Acquisition ; https://hal.archives-ouvertes.fr/hal-00783285 ; A. Villavicencio, T. Poibeau, A. Korhonen and A. Alishahi. Cognitive Aspects of Computational Language Acquisition, Springer, pp.1-26, 2013, Theory and Applications of Natural Language Processing, 978-3-642-31863-4 (2013)
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Multiway Tensor Factorization for Unsupervised Lexical Acquisition
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In: Proceedings of COLING 2012: Technical Papers ; COLING 2012 ; https://hal.archives-ouvertes.fr/hal-00783711 ; COLING 2012, Dec 2012, Mumbai, India. pp.2703-2720 (2012)
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Proceedings of the EACL Workshop on Computational Models of Language Acquisition and Loss
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In: https://hal.archives-ouvertes.fr/hal-00783291 ; A. Villavicencio, T. Poibeau, A. Korhonen & B. Berwick. France. Association for Computational Linguistics, pp.70, 2012, 978-1-937284-19-0 (2012)
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Latent Vector Weighting for Word Meaning in Context
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In: Proceedings of "Empirical Methods in Natural Language Processing" ; Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-00666475 ; Empirical Methods in Natural Language Processing, 2011, France (2011)
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A Weakly-supervised Approach to Argumentative Zoning of Scientific Documents
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In: Actes de la conférence "Empirical Methods in Natural language Processing" (EMNLP) ; Empirical Methods in Natural language Processing (EMNLP) ; https://hal.archives-ouvertes.fr/hal-00666472 ; Empirical Methods in Natural language Processing (EMNLP), 2011, Edinburgh, United Kingdom (2011)
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The acquisition of unrestricted feature-based conceptual representations from corpora
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In: https://hal.archives-ouvertes.fr/hal-00508665 ; 2010 (2010)
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Large-Scale Acquisition of Feature-Based Conceptual Representations from Textual Corpora
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In: Proceedings of the Annual Meeting of the Cognitive Science Society ; The Annual Meeting of the Cognitive Science Society ; https://hal.archives-ouvertes.fr/hal-00507103 ; The Annual Meeting of the Cognitive Science Society, 2010, United States. 6 p (2010)
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Towards unrestricted, large-scale acquisition of feature-based conceptual representations from corpus data
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In: ISSN: 1570-7075 ; EISSN: 1572-8706 ; Research on Language and Computation ; https://hal.archives-ouvertes.fr/hal-00605539 ; Research on Language and Computation, Springer Verlag, 2010, 7 (2-4), pp.137-170 (2010)
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Abstract:
International audience ; In recent years a number of methods have been proposed for the automatic acquisition of feature-based conceptual representations from text corpora. Such methods could offer valuable support for theoretical research on conceptual representation. However, existing methods do not target the full range of concept-relation-feature triples occurring in human-generated norms (e.g. flute produce sound) but rather focus on concept-feature pairs (e.g. flute - sound) or triples involving specific relations only (e.g. is-a or part-of relations). In this article we investigate the challenges that need to be met in both methodology and evaluation when moving towards the acquisition of more comprehensive conceptual representations from corpora. In particular, we investigate the usefulness of three types of knowledge in guiding the extraction process: encyclopedic, syntactic and semantic. We present first a semantic analysis of existing, human-generated feature production norms, which reveals information about co-occurring concept and feature classes. We introduce then a novel method for large-scale feature extraction which uses the class-based information to guide the acquisition process. The method involves extracting candidate triples consisting of concepts, relations and features (e.g. deer have antlers, flute produce sound) from corpus data parsed for grammatical dependencies, and re-weighting the triples on the basis of conditional probabilities calculated from our semantic analysis. We apply this method to an automatically parsed Wikipedia corpus which includes encyclopedic information and evaluate its accuracy using a number of different methods: direct evaluation against the McRae norms in terms of feature types and frequencies, human evaluation, and novel evaluation in terms of conceptual structure variables. Our investigation highlights a number of issues which require addressing in both methodology and evaluation when aiming to improve the accuracy of unconstrained feature extraction further.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [SCCO.COMP]Cognitive science/Computer science; [SCCO.LING]Cognitive science/Linguistics; [SHS.LANGUE]Humanities and Social Sciences/Linguistics
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URL: https://hal.archives-ouvertes.fr/hal-00605539
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