Enhancing Event Extraction from Short Stories through Contextualized Prompts

• URL: http://arxiv.org/abs/2412.10745v1
• Date: Sat, 14 Dec 2024 08:28:52 GMT
• Title: Enhancing Event Extraction from Short Stories through Contextualized Prompts
• Authors: Chaitanya Kirti, Ayon Chattopadhyay, Ashish Anand, Prithwijit Guha,
• Abstract summary: This paper presents textttVrittanta-EN, a collection of 1000 English short stories annotated for real events. Our objective is to clarify the intricate idea of events in the context of short stories. We present fresh guidelines for annotating event mentions and their categories, organized into textitseven distinct classes
• Score: 2.7670701972493568
• License:
• 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 short stories. Towards the objective, we collected 1,000 short stories written mostly for children in the Indian context. Further, we present fresh guidelines for annotating event mentions and their categories, organized into \textit{seven distinct classes}. The classes are {\tt{COGNITIVE-MENTAL-STATE(CMS), COMMUNICATION(COM), CONFLICT(CON), GENERAL-ACTIVITY(GA), LIFE-EVENT(LE), MOVEMENT(MOV), and OTHERS(OTH)}}. Subsequently, we apply these guidelines to annotate the short story dataset. Later, we apply the baseline methods for automatically detecting and categorizing events. We also propose a prompt-based method for event detection and classification. The proposed method outperforms the baselines, while having significant improvement of more than 4\% for the class \texttt{CONFLICT} in event classification task.

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