Abstract
Given the increasing volume of scientific literature in conferences, journals as well as open access websites, it is important to index these data in a hierarchical way for intelligent retrieval. We organized Track 1 in NLPCC2022 Shared Task 5 for multi-label classification for scientific literature. This paper will summarize the task information, the data set, the models returned from the participants and the final result. Furthermore, we will discuss key findings and challenges for hierarchical multi-label classification in the scientific domain.
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Liu, M. et al. (2022). Overview of NLPCC2022 Shared Task 5 Track 1: Multi-label Classification for Scientific Literature. 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_28
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DOI: https://doi.org/10.1007/978-3-031-17189-5_28
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