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- W2904348411 abstract "Fast and correct identification of named entities in queries is crucial for query understanding and to map the query to information in structured knowledge base. Most of the existing works have focused on utilizing search logs and manually curated knowledge bases for entity linking and often involve complex graph operations and are generally slow. We describe a simple, yet fast and accurate, probabilistic entity linking algorithm that can be used in enterprise settings where automatically constructed, domain-specific knowledge graphs are used. In addition to the linked graph structure, textual evidence from the domain-specific corpus is also utilized to improve the performance." @default.
- W2904348411 created "2018-12-22" @default.
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- W2904348411 date "2018-11-27" @default.
- W2904348411 modified "2023-09-23" @default.
- W2904348411 title "Entity Linking in Enterprise Search: Combining Textual and Structural Information" @default.
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- W2904348411 doi "https://doi.org/10.1007/978-3-030-01872-6_8" @default.
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