Using Context Events in Neural Network Models for Event Temporal Status Identification
This work addresses event temporal status identification in natural language processing, representing an incremental improvement over existing methods.
The paper tackles event temporal status identification by extracting dependency chains containing context events as input to neural network models, which consistently outperform previous models using local context words.
Focusing on the task of identifying event temporal status, we find that events directly or indirectly governing the target event in a dependency tree are most important contexts. Therefore, we extract dependency chains containing context events and use them as input in neural network models, which consistently outperform previous models using local context words as input. Visualization verifies that the dependency chain representation can effectively capture the context events which are closely related to the target event and play key roles in predicting event temporal status.