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1
Does referent predictability affect the choice of referential form? A computational approach using masked coreference resolution ...
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Does referent predictability affect the choice of referential form? A computational approach using masked coreference resolution ...
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3
A closer look at scalar diversity using contextualized semantic similarity
In: Sinn und Bedeutung; Bd. 24 Nr. 2 (2020): Proceedings of Sinn und Bedeutung 24; 439-454 ; Proceedings of Sinn und Bedeutung; Vol 24 No 2 (2020): Proceedings of Sinn und Bedeutung 24; 439-454 ; 2629-6055 (2020)
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4
Implying or implicating not both in declaratives and interrogatives
In: Sinn und Bedeutung; Bd. 24 Nr. 2 (2020): Proceedings of Sinn und Bedeutung 24; 423-438 ; Proceedings of Sinn und Bedeutung; Vol 24 No 2 (2020): Proceedings of Sinn und Bedeutung 24; 423-438 ; 2629-6055 (2020)
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5
Don't Blame Distributional Semantics if it can't do Entailment ...
Westera, Matthijs; Boleda, Gemma. - : arXiv, 2019
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6
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue ...
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7
An attention-based explanation for some exhaustivity operators
In: Sinn und Bedeutung; Bd. 21 Nr. 2 (2018): Proceedings of Sinn und Bedeutung 21; 1307-1324 ; Proceedings of Sinn und Bedeutung; Vol 21 No 2 (2018): Proceedings of Sinn und Bedeutung 21; 1307-1324 ; 2629-6055 (2019)
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8
Rising declaratives of the Quality-suspending kind
In: Glossa: a journal of general linguistics; Vol 3, No 1 (2018); 121 ; 2397-1835 (2018)
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9
Ignorance in context: The interaction of modified numerals and QUDs
In: Semantics and Linguistic Theory; Proceedings of SALT 24; 414-431 ; 2163-5951 (2014)
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10
Logic, Language and Meaning : 18th Amsterdam Colloquium, Amsterdam, The Netherlands, December 19-21, 2011, Revised Selected Papers
Aloni, Maria; Kimmelman, Vadim; Roelofsen, Floris. - Berlin, Heidelberg : Springer Berlin Heidelberg, 2012
UB Frankfurt Linguistik
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11
Replies to comments
In: Theoretical linguistics. - Berlin [u.a.] : de Gruyter 38 (2012) 3, 249-264
OLC Linguistik
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12
Event structure, conceptual spaces and the semantics of verbs
In: Theoretical linguistics. - Berlin [u.a.] : de Gruyter 38 (2012) 3, 159-193
OLC Linguistik
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13
Distributional models of category concepts based on names of category members
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14
AMORE-UPF at SemEval-2018 Task 4: BiLSTM with entity library
Westera, Matthijs; Silberer, Carina; Aina, Laura. - : ACL (Association for Computational Linguistics)
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15
TED-Q: TED talks and the questions they evoke
Rohde, Hannah; Westera, Matthijs; Mayol, Laia. - : ACL (Association for Computational Linguistics)
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16
Don’t blame distributional semantics if it can’t do entailment
Westera, Matthijs. - : ACL (Association for Computational Linguistics)
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17
Similarity or deeper understanding?: analyzing the TED-Q dataset of evoked questions
Amidei, Jacopo; Mayol, Laia; Westera, Matthijs. - : ACL (Association for Computational Linguistics)
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18
Humans meet models on object naming: a new dataset and analysis
Silberer, Carina; Westera, Matthijs; Boleda, Gemma. - : ACL (Association for Computational Linguistics)
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19
What do entity-centric models learn? Insights from entity linking in multi-party dialogue
Westera, Matthijs; Silberer, Carina; Aina, Laura; Boleda, Gemma; Sorodoc, Ionut-Teodor. - : ACL (Association for Computational Linguistics)
Abstract: Comunicació presentada a la Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2019), celebrada els dies 2 a 7 de juny de 2019 a Minneapolis, Estats Units d'Amèrica. ; Humans use language to refer to entities in the external world. Motivated by this, in recent years several models that incorporate a bias towards learning entity representations have been proposed. Such entity-centric models have shown empirical success, but we still know little about why. In this paper we analyze the behavior of two recently proposed entity-centric models in a referential task, Entity Linking in Multi-party Dialogue (SemEval 2018 Task 4). We show that these models outperform the state of the art on this task, and that they do better on lower frequency entities than a counterpart model that is not entity-centric, with the same model size. We argue that making models entitycentric naturally fosters good architectural decisions. However, we also show that these models do not really build entity representations and that they make poor use of linguistic context. These negative results underscore the need for model analysis, to test whether the motivations for particular architectures are borne out in how models behave when deployed. ; This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 715154), and from the Spanish Ramón y Cajal programme (grant RYC-2015-18907).
Keyword: Computational linguistics; Computational semantics; Deep learning; Dialogue; Entity linking; Reference
URL: http://hdl.handle.net/10230/42450
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