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A Self-Paced Reading Study on Processing Constructions with Different Degrees of Compositionality
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In: The 35th Annual Conference on Human Sentence Processing ; https://hal.archives-ouvertes.fr/hal-03620795 ; The 35th Annual Conference on Human Sentence Processing, Mar 2022, UC Santa Cruz, United States (2022)
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Does BERT really agree ? Fine-grained Analysis of Lexical Dependence on a Syntactic Task ...
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Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge
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In: Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics ; SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics ; https://hal.archives-ouvertes.fr/hal-03312774 ; SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics, Aug 2021, Online, France. pp.1-11, ⟨10.18653/v1/2021.starsem-1.1⟩ (2021)
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Not all arguments are processed equally: a distributional model of argument complexity
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In: ISSN: 1574-020X ; EISSN: 1574-0218 ; Language Resources and Evaluation ; https://hal.archives-ouvertes.fr/hal-03533181 ; Language Resources and Evaluation, Springer Verlag, 2021, 55 (4), pp.873-900. ⟨10.1007/s10579-021-09533-9⟩ (2021)
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Not all arguments are processed equally: a distributional model of argument complexity
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In: Springer Netherlands (2021)
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Decoding Word Embeddings with Brain-Based Semantic Features ...
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Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge ...
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A comparative evaluation and analysis of three generations of Distributional Semantic Models ...
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Abstract:
Distributional semantics has deeply changed in the last decades. First, predict models stole the thunder from traditional count ones, and more recently both of them were replaced in many NLP applications by contextualized vectors produced by Transformer neural language models. Although an extensive body of research has been devoted to Distributional Semantic Model (DSM) evaluation, we still lack a thorough comparison with respect to tested models, semantic tasks, and benchmark datasets. Moreover, previous work has mostly focused on task-driven evaluation, instead of exploring the differences between the way models represent the lexical semantic space. In this paper, we perform a comprehensive evaluation of type distributional vectors, either produced by static DSMs or obtained by averaging the contextualized vectors generated by BERT. First of all, we investigate the performance of embeddings in several semantic tasks, carrying out an in-depth statistical analysis to identify the major factors influencing ... : Language Resources and Evaluation ...
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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.2105.09825 https://arxiv.org/abs/2105.09825
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Constructional associations trump lexical associations in processing valency coercion
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Common-Sense and Common-Knowledge. How much do Neural Language Models know about the world?
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In: http://etd.adm.unipi.it/theses/available/etd-03122021-000321/ (2021)
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I neologismi nelle edizioni del 2010 e del 2021 del dizionario "Zingarelli della lingua italiana"
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In: http://etd.adm.unipi.it/theses/available/etd-05102021-214231/ (2021)
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Large-scale Cross-lingual Word Sense Disambiguation using Parallel Corpora
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In: http://etd.adm.unipi.it/theses/available/etd-09112021-110903/ (2021)
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Probing the linguistic knowledge of word embeddings: A case study on colexification
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In: http://etd.adm.unipi.it/theses/available/etd-06212021-172428/ (2021)
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"Love is an open door but not a table". Come uomini e macchine 'comprendono' le metafore lessicalizzate e creative.
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In: http://etd.adm.unipi.it/theses/available/etd-03242021-214055/ (2021)
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Le interpretazioni del concetto di composizionalita' delle espressioni idiomatiche nella letteratura psicolinguistica
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In: http://etd.adm.unipi.it/theses/available/etd-09132021-151853/ (2021)
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