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• W2897566602 abstract "Abstract Natural language understanding tasks are key to extracting structured and semantic information from text. One of the most challenging problems in natural language is ambiguity and resolving such ambiguity based on context including temporal information. This paper, focuses on the task of extracting temporal roles from text, e.g. CEO of an organization or head of a state. A temporal role has a domain, which may resolve to different entities depending on the context and especially on temporal information, e.g. CEO of Microsoft in 2000. We focus on the temporal role extraction, as a precursor for temporal role disambiguation. We propose a structured prediction approach based on Conditional Random Fields (CRF) to annotate temporal roles in text and rely on a rich feature set, which extracts syntactic and semantic information from text. We perform an extensive evaluation of our approach based on two datasets. In the first dataset, we extract nearly 400k instances from Wikipedia through distant supervision, whereas in the second dataset, a manually curated ground-truth consisting of 200 instances is extracted from a sample of The New York Times (NYT) articles. Last, the proposed approach is compared against baselines where significant improvements are shown for both datasets." @default.
• W2897566602 created "2018-10-26" @default.
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• W2897566602 date "2018-01-01" @default.
• W2897566602 modified "2023-10-18" @default.
• W2897566602 title "Temporal Role Annotation for Named Entities" @default.
• W2897566602 cites W102708294 @default.
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• W2897566602 doi "https://doi.org/10.1016/j.procs.2018.09.021" @default.
• W2897566602 hasPublicationYear "2018" @default.
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