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Xiong et al., 2018 - Google Patents

Towards better text understanding and retrieval through kernel entity salience modeling

Xiong et al., 2018

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Document ID
17527608943746181834
Author
Xiong C
Liu Z
Callan J
Liu T
Publication year
Publication venue
The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

External Links

Snippet

This paper presents a Kernel Entity Salience Model (KESM) that improves text understanding and retrieval by better estimating entity salience (importance) in documents. KESM represents entities by knowledge enriched distributed representations, models the …
Continue reading at dl.acm.org (PDF) (other versions)

Classifications

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    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30634—Querying
    • G06F17/30657—Query processing
    • G06F17/30675—Query execution
    • G06F17/30684—Query execution using natural language analysis
    • G—PHYSICS
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    • G06F17/30861—Retrieval from the Internet, e.g. browsers
    • G06F17/30864—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
    • G06F17/30867—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
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    • G06F17/30386—Retrieval requests
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    • G06F17/30705—Clustering or classification
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    • G06—COMPUTING; CALCULATING; COUNTING
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