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In this paper, we present a fast and strong neural approach for general purpose text matching applications.
Natural language processing with Python: Analyzing text with the natural language toolkit
Steven Bird, Ewan Klein, and Edward Loper. 2009 · 2009
Earlier work this paper cites.
Natural language processing (almost) from scratch
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Earlier work this paper cites.
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Matthew D Zeiler and Rob Fergus. 2014 · 2014
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Cited alongside, same era.
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