Abstract
In this paper, we present an overview of the NLPCC 2022 Shared Task on Speech Entity Linking. This task aims to study entity linking methods for spoken languages. This speech entity linking task includes two tracks: Entity Recognition and Disambiguation (track 1), Entity Disambiguation-Only (track 2). 20 teams registered in the challenging task, and the top system achieved 0.7460 F1 in track 1 and 0.8884 in track 2. In this paper, we present the task description, dataset description, team submission ranking and results and analyze the results.
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Acknowledgments
This work is supported by the National Key R &D Program of China (No. 2020AAA0106600).
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Song, R., Zhang, S., Tian, X., Guo, Y. (2022). Overview of the NLPCC2022 Shared Task on Speech Entity Linking. In: Lu, W., Huang, S., Hong, Y., Zhou, X. (eds) Natural Language Processing and Chinese Computing. NLPCC 2022. Lecture Notes in Computer Science(), vol 13552. Springer, Cham. https://doi.org/10.1007/978-3-031-17189-5_25
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DOI: https://doi.org/10.1007/978-3-031-17189-5_25
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