Document-level event argument extraction method based on sequence labeling

Abstract

The invention requests to protect a document-level event argument extraction method based on sequence labeling, which comprises the following steps of obtaining Wikipedia priori knowledge related to a corpus entity, and generating a word span entity semantic enhancement embedding representation; splicing the word span entity semantic enhancement embedded representation with a context representation obtained by a pre-training language model to obtain word vector input of an embedded layer; inputting the word representation into a multi-span bidirectional recurrent neural network to obtain a multi-span context feature representation of the word; inputting the multi-span context feature representation into a context attention mechanism module and a gated attention mechanism module, and obtaining a context semantic fusion feature representation of the word; and finally, carrying out event argument extraction on the output feature representation by adopting sequence labeling, and carrying out event argument extraction on an unknown document by utilizing an optimal model obtained by training. According to the method, the extraction effect of the document-level event argument is effectively improved by integrating priori knowledge and multi-span upper and lower semantic feature representation.

Description

technical field [0001] The invention belongs to the field of information extraction of natural language processing, and proposes a document-level event argument extraction model based on sequence annotation. The model can provide basic services for tasks such as knowledge graph construction, relation extraction, information retrieval, and automatic question answering. Background technique [0002] The constituent elements of an event include trigger words, event types, arguments, and argument roles. The goal of event extraction is to automatically obtain the above information from unstructured information and display it in a structured manner. However, most of the traditional event extraction tasks are based on the sentence level, which has obvious defects: an event will involve trigger words and multiple arguments. Usually, very few event trigger words and their related arguments appear in the Ideal situation in the same sentence. Therefore, it is more conducive to the p...

AI Technical Summary

Problems solved by technology

However, most of the traditional event extraction tasks are based on the sentence level, which has obvious defects: an event will involve trigger words and multiple arguments. Usually, very few event trigger words and their related arguments appear in the ideal situation in the same sentence

In addition, previous studies have shown that capturing the semantics of long-sequence text requires an understanding of sentence semantics and inter-sentence semantics, but previous studies have not considered the impact of inter-word semantic information on document-level event argument extraction tasks

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