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Event Argument Extraction using Causal Knowledge Structures by Kar, Debanjana; Sarkar, Sudeshna; Goyal, Pawan is a scholarly article available to read on EtoBox.

What is Event Argument Extraction using Causal Knowledge Structures about?

Event Argument extraction refers to the task of extracting structured information from unstructured text for a particular event of interest. The existing works exhibit poor capabilities to extract causal event arguments like Reason and After Effects. Furthermore, most of the existing works model this task at a sentence level, restricting the context to a local scope. While it may be effective for short spans of text, for longer bodies of text such as news articles, it has often been observed that the arguments for an event do not necessarily occur in the same sentence as that containing an event trigger. To tackle the issue of argument scattering across sentences, the use of global context becomes imperative in this task. In our work, we propose an external knowledge aided approach to infuse document-level event information to aid the extraction of complex event arguments. We develop a causal network for our event-annotated dataset by extracting relevant event causal structures from ConceptNet and phrases from Wikipedia. We use the extracted event causal features in a bi-directional transformer encoder to effectively capture long-range inter-sentence dependencies. We report the eff

Author

Kar, Debanjana; Sarkar, Sudeshna; Goyal, Pawan

Published

2021

Language

EN