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- W2788888652 abstract "Entity Linking is the task of assigning a corresponding entity in a knowledge base to each mention in text. Typically, entity linking is a 3-step process: (1) candidate selection, (2) candidate evaluation, and (3) linking decision. Many existing methods including the state-of-the-art mainly discuss the candidate evaluation step, however, the efficiency of the candidate selection has been barely discussed. This paper discusses the importance of the candidate selection step in order to reduce overall processing time, and proposes a recency-based pruning method for efficient entity linking for time-series documents. Our goal is to reduce processing time of entity linking by efficient and effective pruning of candidate entities without losing accuracy. Our experimental evaluation on a benchmark dataset containing tweet data, shows that our proposal improves the processing time by about 15 times than the existing method without losing accuracy." @default.
- W2788888652 created "2018-03-06" @default.
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- W2788888652 date "2017-12-04" @default.
- W2788888652 modified "2023-09-25" @default.
- W2788888652 title "Recency-based candidate selection for efficient entity linking" @default.
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- W2788888652 doi "https://doi.org/10.1145/3151759.3151771" @default.
- W2788888652 hasPublicationYear "2017" @default.
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