Joint Entity Linking with Deep Reinforcement Learning

By Zheng Fang, Yanan Cao, Dongjie Zhang, Q. Li, Zhenyu Zhang, Yanbing Liu. 2019

108 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research, Natural Language Processing, Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Management Science and Operations Research

In Natural Language Processing

In Reinforcement Learning

Citations in the corpus

Autoregressive Entity Retrieval (2020, neutral): “8 Fang et al. (2019) 94.”
CM3: A Causal Masked Multimodal Model of the Internet (2022, methodology): “Quantitatively we measure the quality of CM3 zero-shot captioning by evaluating using BERT-Score 2 (Zhang et al., 2019) with the RoBERTa-Large models (Liu et al., 2019) on the validation set from MS-COCO.”
Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models (2020, unknown): “…Ganea&Hofmann (2017) [18] 92.22 ± 0.14 Yang et al. (2018) [14] 93.0 Le&Titov (2018) [20] 93.07 ± 0.27 DeepType (2018) [22] 94.88 Fang et al. (2019) [15] 94.3 Le& Titov (2019) [20] 89.66 ± 0.16 DCA-SL (2019)[12] 94.64 ± 0.2 Chen et al. (2020) [10] 93.54 ± 0.12 DCA-SL + Triples(ours) 94.94 ± 0.2…”