Interactive Entity Linking Using Entity-Word Representations

Pei-Chi Lo, Ee-Peng Lim
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引用次数: 3

Abstract

To leverage on entity and word semantics in entity linking, embedding models have been developed to represent entities, words and their context such that candidate entities for each mention can be determined and ranked accurately using their embeddings. To leverage on entity and word semantics in entity linking, embedding models have been developed to represent entities, words and their context such that candidate entities for each mention can be determined and ranked accurately using their embeddings. In this paper, we leverage on human intelligence for embedding-based interactive entity linking. We adopt an active learning approach to select mentions for human annotation that can best improve entity linking accuracy at the same time updating the embedding model. We propose two mention selection strategies based on: (1) coherence of entities linked, and (2) contextual closeness of candidate entities with respect to mention. Our experiments show that our proposed interactive entity linking methods outperform their batch counterpart in all our experimented datasets with relatively small amount of human annotations.
使用实体-词表示的交互式实体链接
为了利用实体链接中的实体和词语义,已经开发了嵌入模型来表示实体、词及其上下文,这样每次提及的候选实体可以使用它们的嵌入来确定和准确排名。为了利用实体链接中的实体和词语义,已经开发了嵌入模型来表示实体、词及其上下文,这样每次提及的候选实体可以使用它们的嵌入来确定和准确排名。在本文中,我们利用人类智能来实现基于嵌入的交互式实体链接。在更新嵌入模型的同时,我们采用主动学习的方法来选择人工标注的提及,以最好地提高实体链接的准确性。我们提出了两种基于(1)关联实体的一致性和(2)候选实体相对于提及的上下文紧密性的提及选择策略。我们的实验表明,我们提出的交互式实体链接方法在我们所有的实验数据集中,在相对较少的人工注释中都优于它们的批处理方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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