Mining Entity Synonyms with Efficient Neural Set Generation
Mining Entity Synonyms with Efficient Neural Set Generation
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DOI：
10.1609/aaai.v33i01.3301249
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Jiaming Shen;Ruiliang Lyu;Xiang Ren;M. Vanni;Brian M. Sadler;Jiawei Han
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文献类型：
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作者：
Jiaming Shen;Ruiliang Lyu;Xiang Ren;M. Vanni;Brian M. Sadler;Jiawei Han
挖掘实体同义词集（即，指代同一实体的术语集）对于许多实体杠杆应用来说是一项重要的任务。以前的工作要么基于它们与给定查询词的相似性对词进行排名，要么将问题视为两阶段任务（即，检测同义词对，随后将这些对组织成同义词集合）。然而，这些方法无法对集合的整体语义进行建模，并且存在错误传播问题。在这里，我们提出了一个新的框架，名为SynSetMine，有效地生成实体同义词集从给定的词汇表，使用的例子集从外部知识库作为远程监督。SynSetMine由两个新颖的模块组成：（1）集合实例分类器，其联合学习如何表示置换不变同义词集合以及是否包含新实例（即，在三个不同领域的真实的数据集上的实验表明，SynSetMine挖掘实体同义词集的有效性和效率。
Mining entity synonym sets (i.e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications. Previous work either rank terms based on their similarity to a given query term, or treats the problem as a two-phase task (i.e., detecting synonymy pairs, followed by organizing these pairs into synonym sets). However, these approaches fail to model the holistic semantics of a set and suffer from the error propagation issue. Here we propose a new framework, named SynSetMine, that efficiently generates entity synonym sets from a given vocabulary, using example sets from external knowledge bases as distant supervision. SynSetMine consists of two novel modules: (1) a set-instance classifier that jointly learns how to represent a permutation invariant synonym set and whether to include a new instance (i.e., a term) into the set, and (2) a set generation algorithm that enumerates the vocabulary only once and applies the learned set-instance classifier to detect all entity synonym sets in it. Experiments on three real datasets from different domains demonstrate both effectiveness and efficiency of SynSetMine for mining entity synonym sets.