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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
BOLT Egyptian Arabic PropBank and Sense -- Discussion Forum, SMS/Chat, and Conversational Telephone Speech
Palmer, Martha; Hwang, Jena D.; Mansouri, Aous. - : Linguistic Data Consortium, 2021. : https://www.ldc.upenn.edu, 2021
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3
What Would a Teacher Do? {P}redicting Future Talk Moves ...
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Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation ...
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BOLT Egyptian Arabic PropBank and Sense -- Discussion Forum, SMS/Chat, and Conversational Telephone Speech ...
Palmer, Martha; Hwang, Jena; Mansouri, Aous. - : Linguistic Data Consortium, 2021
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6
BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine. ...
Majewska, Olga; Collins, Charlotte; Baker, Simon. - : Apollo - University of Cambridge Repository, 2021
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BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine. ...
Majewska, Olga; Collins, Charlotte; Baker, Simon. - : Apollo - University of Cambridge Repository, 2021
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BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine ...
Majewska, Olga; Collins, Charlotte; Baker, Simon. - : Apollo - University of Cambridge Repository, 2021
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9
BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine.
In: nlmid: 101531992 ; essn: 2041-1480 (2021)
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BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine.
Majewska, Olga; Collins, Charlotte; Baker, Simon. - : Springer Science and Business Media LLC, 2021. : J Biomed Semantics, 2021
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BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine
Majewska, Olga; Collins, Charlotte; Baker, Simon. - : BioMed Central, 2021. : Journal of Biomedical Semantics, 2021
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12
Abstract Meaning Representation (AMR) Annotation Release 3.0
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2020. : https://www.ldc.upenn.edu, 2020
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13
BOLT English PropBank and Sense -- Discussion Forum, SMS/Chat, and Conversational Telephone Speech
Palmer, Martha; Hwang, Jena D.; Bonial, Claire. - : Linguistic Data Consortium, 2020. : https://www.ldc.upenn.edu, 2020
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14
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation ...
Wang, Qingyun; Li, Manling; Wang, Xuan. - : arXiv, 2020
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Abstract Meaning Representation (AMR) Annotation Release 3.0 ...
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2020
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16
BOLT English PropBank and Sense -- Discussion Forum, SMS/Chat, and Conversational Telephone Speech ...
Palmer, Martha; Hwang, Jena; Bonial, Claire. - : Linguistic Data Consortium, 2020
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17
Towards collaborative dialogue in Minecraft
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A neural classification method for supporting the creation of BioVerbNet ...
Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Figshare, 2019
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19
A neural classification method for supporting the creation of BioVerbNet ...
Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Figshare, 2019
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20
A neural classification method for supporting the creation of BioVerbNet ...
Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Apollo - University of Cambridge Repository, 2019
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