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Semantics Palmer

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Preview of NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity
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NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity (S16-1110.pdf)

475 KBJohn Philip McCrae ; Kartik Asooja ; Nitish Aggarwal ; Paul BuitelaarEN 6 pages
The paper introduces a multi-feature system for semantic text similarity, utilizing soft alignment and Explicit Semantic Analysis techniques, achieving top performance in 4 out of 5 datasets at SemEval-2016 Task 1.
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Preview of Grounded Language Acquisition
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Grounded Language Acquisition (Ross-et-al_ACL2018_Grounding language acquisition by training semantic parsing using caption videos.pdf)

3.5 MBCandace Ross ; Andrei Barbu ; Yevgeni Berzak ; Battushig Myanganbayar ; Boris KatzEN 10 pages
Researchers developed a semantic parser trained on captioned videos, which learns to convert English sentences into logical forms without annotated sentences,...
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Preview of Annotation of Clinical French Texts with Comprehensive Entity and Relation Scheme
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Annotation of Clinical French Texts with Comprehensive Entity and Relation Scheme (552_Paper.pdf)

180 KBEN 8 pages
Annotated corpora are essential resources for Natural Language Processing applications, providing insight into linguistic and semantic characteristics. However, few such resources exist for languages other than English. This paper presents a high-quality corpus of clinical...
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Preview of Joint Parsing and Semantic Role Labeling: Bridging Gold and Automatic Parses
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Joint Parsing and Semantic Role Labeling: Bridging Gold and Automatic Parses (W05-0636.pdf)

84 KBAssociation for Computational LinguisticsEN 4 pages
Document en en
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Preview of Wikipedia Taxonomy Mapping
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Wikipedia Taxonomy Mapping (IJCAI09-343.pdf)

489 KBSimone Paolo Ponzetto, Roberto NavigliEN 6 pages
Ponzetto and Navigli propose a method to disambiguate Wikipedia categories using WordNet synsets, restructuring the resulting taxonomy. They demonstrate high accuracy in evaluation and enrich WordNet with Wikipedia instances.
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