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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Abstract Meaning Representation (AMR) Annotation Release 3.0 Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2020. : https://www.ldc.upenn.edu, 2020 BASE Show details 2 Abstract Meaning Representation (AMR) Annotation Release 3.0 ... Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2020 BASE Show details 3 Abstract Meaning Representation (AMR) Annotation Release 2.0 Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2017. : https://www.ldc.upenn.edu, 2017 BASE Show details 4 Abstract Meaning Representation (AMR) Annotation Release 2.0 ... Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2017 BASE Show details 5 Abstract Meaning Representation (AMR) Annotation Release 1.0 Knight, Kevin; Baranescu, Laura; Bonial, Claire. - : Linguistic Data Consortium, 2014. : https://www.ldc.upenn.edu, 2014 BASE Show details 6 Abstract Meaning Representation (AMR) Annotation Release 1.0 ... Palmer, Martha; Marcu, Daniel; Griffitt, Kira; Knight, Kevin; Baranescu, Laura; Bonial, Claire; Georgescu, Madalina; Hermjakob, Ulf; Schneider, Nathan. - : Linguistic Data Consortium, 2014 Abstract: Introduction Abstract Meaning Representation (AMR) Annotation Release 1.0 was developed by the Linguistic Data Consortium (LDC), SDL/Language Weaver, Inc., the University of Colorado's Computational Language and Educational Research group and the Information Sciences Institute at the University of Southern California. It contains a sembank (semantic treebank) of over 13,000 English natural language sentences from newswire, weblogs and web discussion forums. AMR captures “who is doing what to whom” in a sentence. Each sentence is paired with a graph that represents its whole-sentence meaning in a tree-structure. AMR utilizes PropBank frames, non-core semantic roles, within-sentence coreference, named entity annotation, modality, negation, questions, quantities, and so on to represent the semantic structure ... URL: https://catalog.ldc.upenn.edu/LDC2014T12 https://dx.doi.org/10.35111/0ync-7404 BASE Hide details

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