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1
Semantic Representations for NLP Using VerbNet and the Generative Lexicon
In: Front Artif Intell (2022)
Abstract: The need for deeper semantic processing of human language by our natural language processing systems is evidenced by their still-unreliable performance on inferencing tasks, even using deep learning techniques. These tasks require the detection of subtle interactions between participants in events, of sequencing of subevents that are often not explicitly mentioned, and of changes to various participants across an event. Human beings can perform this detection even when sparse lexical items are involved, suggesting that linguistic insights into these abilities could improve NLP performance. In this article, we describe new, hand-crafted semantic representations for the lexical resource VerbNet that draw heavily on the linguistic theories about subevent semantics in the Generative Lexicon (GL). VerbNet defines classes of verbs based on both their semantic and syntactic similarities, paying particular attention to shared diathesis alternations. For each class of verbs, VerbNet provides common semantic roles and typical syntactic patterns. For each syntactic pattern in a class, VerbNet defines a detailed semantic representation that traces the event participants from their initial states, through any changes and into their resulting states. The Generative Lexicon guided the structure of these representations. In GL, event structure has been integrated with dynamic semantic models in order to represent the attribute modified in the course of the event (the location of the moving entity, the extent of a created or destroyed entity, etc.) as a sequence of states related to time points or intervals. We applied that model to VerbNet semantic representations, using a class's semantic roles and a set of predicates defined across classes as components in each subevent. We will describe in detail the structure of these representations, the underlying theory that guides them, and the definition and use of the predicates. We will also evaluate the effectiveness of this resource for NLP by reviewing efforts to use the semantic representations in NLP tasks.
Keyword: Artificial Intelligence
URL: https://doi.org/10.3389/frai.2022.821697
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9048683/
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2
Designing Multimodal Datasets for NLP Challenges ...
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3
Representing Inferences and their Lexicalization ...
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4
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis, LOUHI@EMNLP 2020, Online, November 20, 2020.
In: https://halshs.archives-ouvertes.fr/halshs-03102506 ; Association for Computational Linguistics 2020. 2020, 978-1-952148-81-1 ; https://www.aclweb.org/anthology/volumes/2020.louhi-1/ (2020)
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5
Neurosymbolic AI for Situated Language Understanding ...
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6
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation ...
Wang, Qingyun; Li, Manling; Wang, Xuan. - : arXiv, 2020
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7
A Formal Analysis of Multimodal Referring Strategies Under Common Ground ...
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8
A Continuation Semantics for Abstract Meaning Representation ...
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9
Front Matter
In: University of Massachusetts Occasional Papers in Linguistics (2020)
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10
Opacity and the Accessibility of Subject in German A.C.I-Constructions
In: North East Linguistics Society (2020)
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11
Front Matter
In: North East Linguistics Society (2020)
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12
NELS 11 Front Matter
In: North East Linguistics Society (2020)
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13
The lexicon
Pustejovsky, James; Batiukova, Olga. - Cambridge [u.a.] : Cambridge Univ. Press, 2019
Leibniz-Zentrum Allgemeine Sprachwissenschaft
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14
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
In: https://hal.archives-ouvertes.fr/hal-02992403 ; Association for Computational Linguistics. Nov 2019, Hong Kong, 2019, 978-1-950737-77-2 ; https://www.aclweb.org/anthology/D19-6200/ (2019)
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15
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019) ...
Holderness, Eben; Yepes, Antonio Jimeno; Lavelli, Alberto. - : Association for Computational Linguistics, 2019
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16
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
In: Holderness, Eben; Yepes, Antonio Jimeno; Lavelli, Alberto; Minard, Anne-Lyse; Pustejovsky, James; Rinaldi, Fabio (2019). Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019). In: Association for Computational Linguistics, Hong Kong, November, Hong Kong, 2019 - 2019. (2019)
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17
The comparative neuroprimatology 2018 (CNP-2018) road map for research on How the Brain Got Language
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18
Handbook of linguistic annotation ; Volume 2
Ide, Nancy (Herausgeber); Pustejovsky, James (Herausgeber). - Dordrecht : Springer, 2017
Institut für Empirische Sprachwissenschaft
UB Frankfurt Linguistik
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19
Handbook of linguistic annotation ; Volume 1
Ide, Nancy (Herausgeber); Pustejovsky, James (Herausgeber). - Dordrecht : Springer, 2017
Institut für Empirische Sprachwissenschaft
UB Frankfurt Linguistik
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20
Handbook of Linguistic Annotation
Ide, Nancy (Hrsg.); Pustejovsky, James (Hrsg.). - Berlin / New York : Springer, 2017
IDS Bibliografie zur Gesprächsforschung
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