Empirical study on temporal expression extraction via large language model

Haolei Wu

doi:10.1117/12.3035007

11 July 2024 Empirical study on temporal expression extraction via large language model

Haolei Wu

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132102I (2024) https://doi.org/10.1117/12.3035007
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Temporal expression extraction is to identify temporal expressions from raw text. Predominant methods rely on handcrafted rules or high-quality training datasets, and currently few works utilize large language models in this task. This paper demonstrated the ability of large language models to understand temporal information by building few-shot temporal expression extraction system that does not require hand-crafted rules or training. To reduce model output length, the system used phrase-only output schema instead of outputting entire sequence. The model also alleviated hallucination effect by prompting the model to predict expression type and using the model itself to filter model outputs. Experimental results indicated that the model achieved decent performance without training or rules.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haolei Wu "Empirical study on temporal expression extraction via large language model", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132102I (11 July 2024); https://doi.org/10.1117/12.3035007

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available