学习使用本地和分布式文本表示进行Web搜索匹配
Learning to Match Using Local and Distributed Representations of Text for Web Search
摘要：
诸如潜在语义分析和基于神经嵌入的模型学习文本的分布式表示，并将查询与潜在语义空间中的文档进行匹配。另一方面，在传统的信息检索模型中，术语具有离散或局部表示，并且文档的相关性由查询术语在正文中的精确匹配来确定。我们假设，匹配与分布式表示补充匹配与传统的本地表示，两者的组合是有利的。我们提出了一种新的文档排序模型，该模型由两个独立的深度神经网络组成，一个使用本地表示匹配查询和文档，另一个使用学习的分布式表示匹配查询和文档。这两个网络作为单个神经网络的一部分进行联合训练。我们表明，这种组合或“二重奏”的表现显着优于任何一个神经网络单独的网页排名任务，也显着优于传统的基线和其他最近提出的模型的基础上神经网络。
英文摘要：
Models such as latent semantic analysis and those based on neural embeddings learn distributed representations of text, and match the query against the document in the latent semantic space. In traditional information retrieval models, on the other hand, terms have discrete or local representations, and the relevance of a document is determined by the exact matches of query terms in the body text. We hypothesize that matching with distributed representations complements matching with traditional local representations, and that a combination of the two is favorable. We propose a novel document ranking model composed of two separate deep neural networks, one that matches the query and the document using a local representation, and another that matches the query and the document using learned distributed representations. The two networks are jointly trained as part of a single neural network. We show that this combination or `duet' performs significantly better than either neural network individually on a Web page ranking task, and also significantly outperforms traditional baselines and other recently proposed models based on neural networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
