NeuPL: Attention-based Semantic Matching and Pair-Linking for Entity Disambiguation

Minh C. Phan, Aixin Sun, Yi Tay, Jialong Han, Chenliang Li
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引用次数: 50

Abstract

Entity disambiguation, also known as entity linking, is the task of mapping mentions in text to the corresponding entities in a given knowledge base, e.g. Wikipedia. Two key challenges are making use of mention's context to disambiguate (i.e. local objective), and promoting coherence of all the linked entities (i.e. global objective). In this paper, we propose a deep neural network model to effectively measure the semantic matching between mention's context and target entity. We are the first to employ the long short-term memory (LSTM) and attention mechanism for entity disambiguation. We also propose Pair-Linking, a simple but effective and significantly fast linking algorithm. Pair-Linking iteratively identifies and resolves pairs of mentions, starting from the most confident pair. It finishes linking all mentions in a document by scanning the pairs of mentions at most once. Our neural network model combined with Pair-Linking, named NeuPL, outperforms state-of-the-art systems over different types of documents including news, RSS, and tweets.
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基于注意的语义匹配和实体消歧的对链接
实体消歧,也称为实体链接,是将文本中的提及映射到给定知识库中相应实体的任务,例如维基百科。两个关键的挑战是利用提及的上下文来消除歧义(即局部目标),并促进所有相关实体的一致性(即全球目标)。在本文中,我们提出了一种深度神经网络模型来有效地度量提及上下文与目标实体之间的语义匹配。我们首次采用长短期记忆(LSTM)和注意机制进行实体消歧。我们还提出了一种简单有效且速度显著的链接算法Pair-Linking。成对链接迭代地识别和解析成对的提及,从最自信的一对开始。它通过最多扫描一次提及对来完成文档中所有提及的链接。我们的神经网络模型结合Pair-Linking,称为NeuPL,在不同类型的文档(包括新闻、RSS和tweet)上优于最先进的系统。
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