Abstract
Entity linking is a task of linking mentions in text to the corresponding entities in a knowledge base. Recently, entity linking has received considerable attention and several online entity linking systems have been published. In this paper, we build an online bilingual entity linking system XLink, which is based on Wikipeida and Baidu Baike. XLink conducts two steps to link the mentions in the input document to entities in knowledge base, namely mention parsing and entity disambiguation. To eliminate dependency of language, we conduct mention parsing without any named entity recognition tools. To ensure the correctness of linking results, we propose an unsupervised generative probabilistic method and utilize text and knowledge joint representations to perform entity disambiguation. Experiments show that our system gets a state-of-the-art performance and a high time efficiency.
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Acknowledgements
The work is supported by 973 Program (No. 2014CB340504), NSFC key project (No. 61533018, 61661146007), Fund of Online Education Research Center, Ministry of Education (No. 2016ZD102), THUNUS NExT Co-Lab, National Natural Science Foundation of China (Grant No. 61375054) and Natural Science Foundation of Guangdong Province (Grant No. 2014A030313745).
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Zhang, J., Cao, Y., Hou, L., Li, J., Zheng, HT. (2017). XLink: An Unsupervised Bilingual Entity Linking System. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2017 2017. Lecture Notes in Computer Science(), vol 10565. Springer, Cham. https://doi.org/10.1007/978-3-319-69005-6_15
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