Joint Entity Linking with Deep Reinforcement Learning

Abstract

Entity linking is the task of aligning mentions to corresponding entities in a given knowledge base. Previous studies have highlighted the necessity for entity linking systems to capture the global coherence. However, there are two common weaknesses in previous global models. First, most of them calculate the pairwise scores between all candidate entities and select the most relevant group of entities as the final result. In this process, the consistency among wrong entities as well as that among right ones are involved, which may introduce noise data and increase the model complexity. Second, the cues of previously disambiguated entities, which could contribute to the disambiguation of the subsequent mentions, are usually ignored by previous models. To address these problems, we convert the global linking into a sequence decision problem and propose a reinforcement learning model which makes decisions from a global perspective. Our model makes full use of the previous referred entities and explores the long-term influence of current selection on subsequent decisions. We conduct experiments on different types of datasets, the results show that our model outperforms state-of-the-art systems and has better generalization performance.

10 pages, 5 figures

References in corpus (7)

• DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
• Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation
• Neural Collective Entity Linking
• Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation
• Improving Entity Linking by Modeling Latent Relations between Mentions
• Deep Reinforcement Learning for Chinese Zero pronoun Resolution
• Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All