Abstract
Entity Linking (EL) is a task that links entity mentions in the text to corresponding entities in a knowledge base. The key to building a high-quality EL system involves accurate representations of word and entity. In this paper, we propose an attention-based bilinear joint learning framework for entity linking. First, a novel encoding method is employed for coding EL. This method jointly learns words and entities using an attention mechanism. Next, for ranking features, a weighted summation model is introduced to model the textual context and coherence. Then, we employ a pairwise boosting regression tree (PBRT) to rank candidate entities. As input, PBRT takes both features constructed with a weighted summation model and conventional EL features. Finally, through the experiment, we demonstrate that the proposed model learns embedding efficiently and improves the EL performance compared with other state-of-the-art methods. Our approach achieves superior result on two standard EL datasets: CoNLL and TAC 2010.
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Acknowledgements
This work is supported by the National Key Research and Development Plan of China under Grant No. 2017YFD0400101, the Natural Science Foundation of Shanghai under Grant No. 16ZR1411200, and the CERNET Innovation Project under Grant No. NGII20170513.
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Cao, M., Wang, P., Gao, H., Shi, J., Tao, Y., Zhang, W. (2019). Attention-Based Bilinear Joint Learning Framework for Entity Linking. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-030-30146-0_17
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