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Taxonomy Completion via Triplet Matching Network
arXiv - CS - Information Retrieval Pub Date : 2021-01-06 , DOI: arxiv-2101.01896
Jieyu Zhang, Xiangchen Song, Ying Zeng, Jiaze chen, Jiaming Shen, Yuning Mao, Lei Li

Automatically constructing taxonomy finds many applications in e-commerce and web search. One critical challenge is as data and business scope grow in real applications, new concepts are emerging and needed to be added to the existing taxonomy. Previous approaches focus on the taxonomy expansion, i.e. finding an appropriate hypernym concept from the taxonomy for a new query concept. In this paper, we formulate a new task, "taxonomy completion", by discovering both the hypernym and hyponym concepts for a query. We propose Triplet Matching Network (TMN), to find the appropriate pairs for a given query concept. TMN consists of one primal scorer and multiple auxiliary scorers. These auxiliary scorers capture various fine-grained signals (e.g., query to hypernym or query to hyponym semantics), and the primal scorer makes a holistic prediction on triplet based on the internal feature representations of all auxiliary scorers. Also, an innovative channel-wise gating mechanism that retains task-specific information in concept representations is introduced to further boost model performance. Experiments on four real-world large-scale datasets show that TMN achieves the best performance on both taxonomy completion task and the previous taxonomy expansion task, outperforming existing methods.

中文翻译:

通过三元组匹配网络完成分类

自动构建分类法可在电子商务和Web搜索中找到许多应用。一项关键挑战是,随着实际应用中数据和业务范围的增长,新概念不断涌现,需要将其添加到现有分类法中。先前的方法集中于分类法扩展,即,从分类法中找到合适的上位词概念以用于新的查询概念。在本文中,我们通过发现查询的上位词和下位词概念来制定新任务“分类完成”。我们建议使用三重匹配网络(TMN),以找到合适的给定查询概念对。TMN由一个主要得分手和多个辅助得分手组成。这些辅助计分器捕获各种细粒度的信号(例如,查询上位词或查询下位词语义),并且原始记分员对基于所有辅助记分员的内部特征表示的三元组。此外,引入了一种创新的基于通道的门控机制,该机制在概念表示中保留了特定于任务的信息,以进一步提高模型的性能。在四个真实世界的大型数据集上进行的实验表明,TMN在分类法完成任务和先前的分类法扩展任务上均达到最佳性能,优于现有方法。
更新日期:2021-01-07
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