Abstract
We present a neural-encoded mention-hypergraph (named as NEMH in this paper) model for mention-extraction and classification in this paper. Through extraction of textual mention entities, a model is proposed that applies a hypergraph-encoding schema to neural networks. Comparing the results of the proposed model with the previous approaches, the proposed model can thus identify unlimited-length nested mention entities, which is a major milestone in the field. Several experiments are conducted on many datasets used in the baseline approaches, and the obtained results indicated that the designed model has high effectiveness compared to the existing models.
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Lin, J.CW., Wu, J.MT., Shao, Y., Pirouz, M., Zhang, B. (2019). An Effective BI-encoded Schema for Mention Extraction. In: Lin, JW., Ting, IH., Tang, T., Wang, K. (eds) Multidisciplinary Social Networks Research. MISNC 2019. Communications in Computer and Information Science, vol 1131. Springer, Singapore. https://doi.org/10.1007/978-981-15-1758-7_5
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DOI: https://doi.org/10.1007/978-981-15-1758-7_5
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