Event Argument Extraction using Causal Knowledge Structures

• URL: http://arxiv.org/abs/2105.00477v1
• Date: Sun, 2 May 2021 13:59:07 GMT
• Title: Event Argument Extraction using Causal Knowledge Structures
• Authors: Debanjana Kar, Sudeshna Sarkar, Pawan Goyal
• Abstract summary: Event Argument extraction refers to the task of extracting structured information from unstructured text for a particular event of interest. Most of the existing works model this task at a sentence level, restricting the context to a local scope. We propose an external knowledge aided approach to infuse document-level event information to aid the extraction of complex event arguments.
• Score: 9.56216681584111
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 effectiveness of our proposed approach through both qualitative and quantitative analysis. In this task, we establish our findings on an event annotated dataset in 5 Indian languages. This dataset adds further complexity to the task by labelling arguments of entity type (like Time, Place) as well as more complex argument types (like Reason, After-Effect). Our approach achieves state-of-the-art performance across all the five languages. Since our work does not rely on any language-specific features, it can be easily extended to other languages.

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