Improving Term Extraction with Terminological Resources
Sophie Aubin, Thierry Hamon
Abstract
Studies of different term extractors on a corpus of the biomedical domain revealed decreasing performances when applied to highly technical texts. The difficulty or impossibility of customising them to new domains is an additional limitation. In this paper, we propose to use external terminologies to influence generic linguistic data in order to augment the quality of the extraction. The tool we implemented exploits testified terms at different steps of the process: chunking, parsing and extraction of term candidates. Experiments reported here show that, using this method, more term candidates can be acquired with a higher level of reliability. We further describe the extraction process involving endogenous disambiguation implemented in the term extractor YaTeA.
Create a lesson
Related papers
CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes
Yufan Wu, Yinghui He, Zhengyi Hu et al.
TTPO: Test-Time Policy Optimization
Aozhe Wang, Zhengxi Lu, Jianze Wang et al.
Stochastic Estimation of Transduced Language Models
Vésteinn Snæbjarnarson, Samuel Kiegeland, Manuel de Prada Corral et al.
Boosting LLM Exploration via Weak-Model Guidance in RLVR
Xingyu Shen, Huishuai Zhang, Peng Li et al.
Consolidating RLVR Capabilities Across Domains: A Deep Dive into Fusion Paradigms
Siye Wu, Kai Yang, Yuchen Cai et al.
How Language Models Organize and Structure Moral Knowledge
Orion Reblitz-Richardson