Abstract
In recent years, financial events in the stock market have increased dramatically. Extracting valuable information automatically from massive financial documents can provide effective support for the analysis of financial events. This paper just proposes an end-to-end document-level subject pair recognition method. It aims to recognize the subject pair, i.e. the subject and the object of an event. Given one document and the predefined event type set, this method will output all the corresponding subject pairs related to each event type. Subject pair recognition is certainly a document-level extraction task since it needs to scan the entire document to output desired subject pairs. This paper constructs a global document-level vector based on sentence-level vectors which are encoded from BERT. The global document-level vector aims to cover the information carried by the entire document. It is utilized to guide the extraction process conducted sentence by sentence. After considering global information, our method obtains superior experimental results.
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Acknowledgement
The research in this article is supported by the Science and Technology Innovation 2030 - “New Generation Artificial Intelligence” Major Project (2018AA0101901), the National Key Research and Development Project (2018YFB1005103), the Key Project of National Science Foundation of China (61632011), the National Science Foundation of China (61772156, 61976073) and the Foundation of Heilongjiang Province (F2018013).
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Hu, Z., Liu, M., Wu, Y., Xu, J., Qin, B., Li, J. (2020). Document-Level Event Subject Pair Recognition. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12430. Springer, Cham. https://doi.org/10.1007/978-3-030-60450-9_23
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DOI: https://doi.org/10.1007/978-3-030-60450-9_23
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