Document level event detection model based on FinBERT and Bi-GRU
The existing document level event detection methods are generally used to perform two sub-tasks:candidate enti-ty extraction and event detection,and the precision of candidate entity extraction affects the results of event detection.A docu-ment level event detection model based on FinBERT and Bi-GRU is proposed against the insufficient accuracy of the existing methods of candidate entity extraction.Firstly,FinBERT pre-training model is used to improve the representation ability of word embedding vector to financial semantics,so as to enhance the perception of financial semantics of the model.Then,the semantic representation obtained from the previous layer is input into the Bi-GRU model and multi-head attention mechanism to capture global and local features,decode and mark the candidate entities and entity types through CRF.Finally,according to the extract-ed candidate entity information,the predefined events in the document are judged.The experimental results show that the model improves the precision of the candidate entity extraction task and obtains better document level event detection results.
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