判别式
利用
水准点(测量)
计算机科学
背景(考古学)
匹配(统计)
嵌入
机器学习
情报检索
人工智能
数据科学
统计
大地测量学
古生物学
生物
计算机安全
数学
地理
作者
Dongxiang Zhang,Yuyang Nie,Sai Wu,Yanyan Shen,Kian-Lee Tan
出处
期刊:The Web Conference
日期:2020-04-20
被引量:27
标识
DOI:10.1145/3366423.3380017
摘要
Entity matching (EM) is a classic research problem that identifies data instances referring to the same real-world entity. Recent technical trend in this area is to take advantage of deep learning (DL) to automatically extract discriminative features. DeepER and DeepMatcher have emerged as two pioneering DL models for EM. However, these two state-of-the-art solutions simply incorporate vanilla RNNs and straightforward attention mechanisms. In this paper, we fully exploit the semantic context of embedding vectors for the pair of entity text descriptions. In particular, we propose an integrated multi-context attention framework that takes into account self-attention, pair-attention and global-attention from three types of context. The idea is further extended to incorporate attribute attention in order to support structured datasets. We conduct extensive experiments with 7 benchmark datasets that are publicly accessible. The experimental results clearly establish our superiority over DeepER and DeepMatcher in all the datasets.
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