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pro vyhledávání: '"Wan, Shengxian"'
Deep neural networks have achieved significant improvements in information retrieval (IR). However, most existing models are computational costly and can not efficiently scale to long documents. This paper proposes a novel End-to-End neural ranking f
Externí odkaz:
http://arxiv.org/abs/1906.09404
Semantic matching, which aims to determine the matching degree between two texts, is a fundamental problem for many NLP applications. Recently, deep learning approach has been applied to this problem and significant improvements have been achieved. I
Externí odkaz:
http://arxiv.org/abs/1604.04378
Matching two texts is a fundamental problem in many natural language processing tasks. An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score. Inspired by the success of convolutio
Externí odkaz:
http://arxiv.org/abs/1602.06359
Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, su
Externí odkaz:
http://arxiv.org/abs/1511.08277
Akademický článek
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Publikováno v:
Proceedings of the 36th International ACM Sigir Conference Research & Development in Information Retrieval; 7/28/2013, p1045-1048, 4p
