Abstract
In this paper, we propose a unified information extraction system, which handles event extraction (EE) and relation extraction (RE) tasks. Given context and schema, event extraction aims to extract the events and the specific roles in the events, and relation extraction extracts all SPO triples. We formulate event extraction and relation extraction as one extraction schema, that is, role recognition and role combination. We use Multi-Label Pointer Network (MLPN) to recognize composite roles that contain both event/relation and role information and simultaneously train a Co-occurrence Matrix (CM) to determine the co-occurrence relationship of composite roles, i.e., whether two roles describe the same event/relation. Using such a Unified model based on Role Recognition and Combination (URRC) and corresponding combination strategy, we implement three tasks: sentence-level event extraction, document-level event extraction, and relation extraction. In LIC 2021, our model achieved 6th in the Multi-format Information Extraction racing track with an average \(F_1\) score of 77.44% in the final test dataset of three subtasks.
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Zhang, Y., Lan, M. (2021). A Unified Information Extraction System Based on Role Recognition and Combination. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13029. Springer, Cham. https://doi.org/10.1007/978-3-030-88483-3_36
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DOI: https://doi.org/10.1007/978-3-030-88483-3_36
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