Abstract
Entity Linking (EL) is the task of mapping mentions in natural-language text to their corresponding entities in a knowledge base (KB). Type modeling for mention and entity could be beneficial for entity linking. In this paper, we propose a type-guided semantic embedding approach to boost collective entity linking. We use Bidirectional Long Short-Term Memory (BiLSTM) and dynamic convolutional neural network (DCNN) to model the mention and the entity respectively. Then, we build a graph with the semantic relatedness of mentions and entities for the collective entity linking. Finally, we evaluate our approach by comparing the state-of-the-art entity linking approaches over a wide range of very different data sets, such as TAC-KBP from 2009 to 2013, AIDA, DBPediaSpotlight, N3-Reuters-128, and N3-RSS-500. Besides, we also evaluate our approach with a Chinese Corpora. The experiments reveal that the modeling for entity type can be very beneficial to the entity linking.
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Acknowledgements
This work is supported by the Zhejiang Provincial Natural Science Foundation of China (No. LY17F020015), the Chinese Knowledge Center of Engineering Science and Technology (CKCEST), and the Fundamental Research Funds for the Central Universities (No. 2017FZA5016).
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Lu, W., Zhou, Y., Lu, H., Ma, P., Zhang, Z., Wei, B. (2018). Boosting Collective Entity Linking via Type-Guided Semantic Embedding. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_45
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