Character Attribute Extraction from Movie Scripts Using LLMs

Narrative understanding is an integrative task of studying characters, plots, events, and relations in a story. It involves natural language processing tasks such as named entity recognition and coreference resolution to identify the characters, semantic role labeling and argument mining to find character actions and events, information extraction and question answering to describe character attributes, causal analysis to relate different events, and summarization to find the main storyline. In this work, we aim to formally operationalize the task of character attribute extraction, motivated by analyzing inclusive character representations and portrayals. We focus on a mix of static and dynamic attribute types that require varying context sizes for their accurate retrieval. We use automated screenplay parsing, entity recognition, and external knowledge bases to collect character descriptions from movie scripts, and explore different prompting strategies (zero-shot, few-shot, and chain-of-thought) to leverage large language models for attribute extraction.
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