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1
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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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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3
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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4
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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5
InfoForager: Leveraging Semantic Search with AMR for COVID-19 Research ...
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6
Abstract Meaning Representation (AMR) Annotation Release 3.0 ...
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2020
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7
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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8
Graph-to-Graph Meaning Representation Transformations for Human-Robot Dialogue
In: Proceedings of the Society for Computation in Linguistics (2020)
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9
Abstract Meaning Representation for Human-Robot Dialogue
In: Proceedings of the Society for Computation in Linguistics (2019)
Abstract: In this research, we begin to tackle the challenge of natural language understanding (NLU) in the context of the development of a robot dialogue system. We explore the adequacy of Abstract Meaning Representation (AMR) as a conduit for NLU. First, we consider the feasibility of using existing AMR parsers for automatically creating meaning representations for robot-directed transcribed speech data. We evaluate the quality of output of two parsers on this data against a manually annotated gold-standard data set. Second, we evaluate the semantic coverage and distinctions made in AMR overall: how well does it capture the meaning and distinctions needed in our collaborative human-robot dialogue domain? We find that AMR has gaps that align with linguistic information critical for effective human-robot collaboration in search and navigation tasks, and we present task-specific modifications to AMR to address the deficiencies.
Keyword: Computational Linguistics; Dialogue systems; Natural Language Understanding; semantics
URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1102&context=scil
https://scholarworks.umass.edu/scil/vol2/iss1/25
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10
Annotated corpora and tools of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions (edition 1.1)
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11
Abstract Meaning Representation (AMR) Annotation Release 2.0
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2017. : https://www.ldc.upenn.edu, 2017
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12
Abstract Meaning Representation (AMR) Annotation Release 2.0 ...
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2017
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13
Abstract Meaning Representation (AMR) Annotation Release 1.0
Knight, Kevin; Baranescu, Laura; Bonial, Claire. - : Linguistic Data Consortium, 2014. : https://www.ldc.upenn.edu, 2014
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14
Abstract Meaning Representation (AMR) Annotation Release 1.0 ...
Palmer, Martha; Marcu, Daniel; Griffitt, Kira. - : Linguistic Data Consortium, 2014
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