语义匹配的深度学习:综述

Han Li, Yash Govind, Sidharth Mudgal, Theodoros Rekatsinas, A. Doan
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引用次数: 2

摘要

语义匹配查找模式/数据结构之间的某些类型的语义关系。例子包括实体匹配、实体链接、共引用解析、模式/本体匹配、语义文本相似度、文本蕴涵、问答、标记等。语义匹配在数据库、AI、KDD、Web和语义Web社区中受到了广泛的关注。近年来,许多研究也将深度学习应用于语义匹配。本文对这一快速发展的课题进行了综述。我们定义了语义匹配问题,将其变体分类到一个分类法中,并描述了重要的应用。我们描述了语义匹配的重要变化的深度学习解决方案。最后,讨论了未来的研发方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DEEP LEARNING FOR SEMANTIC MATCHING: A SURVEY
Semantic matching finds certain types of semantic relationships among schema/data constructs. Examples include entity matching, entity linking, coreference resolution, schema/ontology matching, semantic text similarity, textual entailment, question answering, tagging, etc. Semantic matching has received much attention in the database, AI, KDD, Web, and Semantic Web communities. Recently, many works have also applied deep learning (DL) to semantic matching. In this paper we survey this fast growing topic. We define the semantic matching problem, categorize its variations into a taxonomy, and describe important applications. We describe DL solutions for important variations of semantic matching. Finally, we discuss future R\&D directions.
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