Abstract
This paper presents an overview of the NLPCC 2022 shared task 5 track 2, i.e., Named Entity Recognition (NER), which aims at extracting entities of interest from domain-specific texts (material science). The task provides 5600 labeled sentences (with the BIO tagging format) collected from ACS material science publications. Participants are required to train a NER model with these labeled sentences to automatically extract entities of material science. 47 teams registered and 19 of them submitted the results; the results are summarized in the evaluation section. The best-submitted model shows around 0.07 improvement with respect to \(F_{1}score\) over the baseline BiLSTM-CRF model.
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Cai, B. et al. (2022). Overview of NLPCC2022 Shared Task 5 Track 2: Named Entity Recognition. 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_30
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DOI: https://doi.org/10.1007/978-3-031-17189-5_30
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