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• W2898843412 abstract "Most neural Information Retrieval (Neu-IR) models derive query-to-document ranking scores based on term-level matching. Inspired by TileBars, a classical term distribution visualization method, in this paper, we propose a novel Neu-IR model that handles query-to-document matching at the subtopic and higher levels. Our system first splits the documents into topical segments, “visualizes” the matchings between the query and the segments, and then feeds an interaction matrix into a Neu-IR model, DeepTileBars, to obtain the final ranking scores. DeepTileBars models the relevance signals occurring at different granularities in a document’s topic hierarchy. It better captures the discourse structure of a document and thus the matching patterns. Although its design and implementation are light-weight, DeepTileBars outperforms other state-of-the-art Neu-IR models on benchmark datasets including the Text REtrieval Conference (TREC) 2010-2012 Web Tracks and LETOR 4.0." @default.
• W2898843412 created "2018-11-09" @default.
• W2898843412 creator A5002485529 @default.
• W2898843412 creator A5042237237 @default.
• W2898843412 date "2019-07-17" @default.
• W2898843412 modified "2023-10-11" @default.
• W2898843412 title "DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval" @default.
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• W2898843412 doi "https://doi.org/10.1609/aaai.v33i01.3301289" @default.
• W2898843412 hasPublicationYear "2019" @default.
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