Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 14th June 2025, 06:26:37pm WEST
Session Chair: Yutong Yang, Shanghai Jiao Tong University
Location:Aud B3 (TB)
152 places
Presentations
Towards an Evaluation Framework for Assessing Large Language Models in Text Encoding
Sabrina Strutz, Georg Vogeler
University of Graz, Austria
This contribution proposes a multifaceted evaluation framework for assessing the performance of Large Language Models (LLMs) in encoding historical letters according to the TEI Guidelines, using the Joseph von Hammer-Purgstall correspondence edition as a case study.
Investigating Conceptual Plasticity: On Detecting a Re-Conceptualization of Focalization with Large Language Models
Axel Pichler1, Janis Pagel2
1University of Vienna, Austria; 2University of Cologne, Germany
We investigate the extent to which LLMs are able to learn a redefinition of a concept from literary studies, focalization, and apply it adequately to text examples. It shows that, with one exception, there are no statistically significant differences between the LLM output for prompts with and without the redefinition.
Automated Extraction of Character Features in Fiction: Comparing Bert-based Models and Large Language Models on Fanfiction in English and Chinese
Xiaoyan Yang, Federico Pianzola
University of Groningen, Netherlands, The
Aiming to study cross-cultural narrative patterns, this research develops a computational framework for extracting character features from English and Chinese fanfiction. By evaluating traditional Bert-based models and LLMs on tasks including character recognition, coreference resolution, dialogue and trait extraction, it provides insights into NLP tools' performance in characterization analysis.
Automatic Tagging of Word Senses for a Large-Scale Historical Japanese Corpus
Soma Asada1, Kanako Komiya1, Masayuki Asahara2
1Tokyo University of Agriculature and Technology, Japan; 2NINJAL, Japan
We developed a system to automatically assign word sense tags to all content words in a substantial historical Japanese corpus, comprising over 20 million words. Our approach leverages a system based on Bidirectional Encoder Representations from Transformers (BERT), achieving an accuracy of 88.57%.
Leveraging Human Expertise for LLM-Assisted Dialogue Character Extraction and Attribution in Classic Chinese Novels
1Shanghai Jiao Tong University, People's Republic of China; 2Peking University, People's Republic of China
In this work, we propose a framework for extracting, annotating, attributing and visualizing dialogue characters in classic Chinese novels. We leverage interactive workflows to incorporate expert’s knowledge in the dialogue character extraction and attribution process.
