Enhancing Event Extraction from Short Stories through Contextualized Prompts
Chaitanya Kirti (1), Ayon Chattopadhyay (1), Ashish Anand (1), Prithwijit Guha (1) ((1) Indian Institute of Technology Guwahati)

TL;DR
This paper introduces exttt{Vrittanta-EN}, a new annotated dataset of 1000 Indian children's short stories for event extraction, and proposes a prompt-based method that improves event detection and classification, especially for conflict events.
Contribution
The paper provides a novel annotated dataset for literary event extraction and introduces a prompt-based approach that outperforms baselines in this domain.
Findings
Prompt-based method improves event classification accuracy.
Significant 4%+ gain in conflict event detection.
New guidelines for annotating literary events.
Abstract
Event extraction is an important natural language processing (NLP) task of identifying events in an unstructured text. Although a plethora of works deal with event extraction from new articles, clinical text etc., only a few works focus on event extraction from literary content. Detecting events in short stories presents several challenges to current systems, encompassing a different distribution of events as compared to other domains and the portrayal of diverse emotional conditions. This paper presents \texttt{Vrittanta-EN}, a collection of 1000 English short stories annotated for real events. Exploring this field could result in the creation of techniques and resources that support literary scholars in improving their effectiveness. This could simultaneously influence the field of Natural Language Processing. Our objective is to clarify the intricate idea of events in the context of…
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Artificial Intelligence in Games
MethodsFocus
