@inproceedings{swarup-etal-2025-syntax,
title = "From Syntax to Semantics: Evaluating the Impact of Linguistic Structures on {LLM}-Based Information Extraction",
author = "Swarup, Anushka and
Bhandarkar, Avanti and
Wilson, Ronald and
Pan, Tianyu and
Woodard, Damon",
editor = "Fei, Hao and
Tu, Kewei and
Zhang, Yuhui and
Hu, Xiang and
Han, Wenjuan and
Jia, Zixia and
Zheng, Zilong and
Cao, Yixin and
Zhang, Meishan and
Lu, Wei and
Siddharth, N. and
{\O}vrelid, Lilja and
Xue, Nianwen and
Zhang, Yue",
booktitle = "Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)",
month = aug,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.xllm-1.5/",
doi = "10.18653/v1/2025.xllm-1.5",
pages = "36--48",
ISBN = "979-8-89176-286-2",
abstract = "Large Language Models (LLMs) have brought significant breakthroughs across all areas of Natural Language Processing (NLP), including Information Extraction (IE). However, knowledge gaps remain regarding their effectiveness in extracting entity-relation triplets, i.e. Joint Relation Extraction (JRE). JRE has been a key operation in creating knowledge bases that can be used to enhance Retrieval Augmented Generation (RAG) systems. Prior work highlights low-quality triplets generated by LLMs. Thus, this work investigates the impact of incorporating linguistic structures, such as constituency and dependency trees and semantic role labeling, to enhance the quality of the extracted triplets. The findings suggest that incorporating specific structural information enhances the uniqueness and topical relevance of the triplets, particularly in scenarios where multiple relationships are present."
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%0 Conference Proceedings
%T From Syntax to Semantics: Evaluating the Impact of Linguistic Structures on LLM-Based Information Extraction
%A Swarup, Anushka
%A Bhandarkar, Avanti
%A Wilson, Ronald
%A Pan, Tianyu
%A Woodard, Damon
%Y Fei, Hao
%Y Tu, Kewei
%Y Zhang, Yuhui
%Y Hu, Xiang
%Y Han, Wenjuan
%Y Jia, Zixia
%Y Zheng, Zilong
%Y Cao, Yixin
%Y Zhang, Meishan
%Y Lu, Wei
%Y Siddharth, N.
%Y Øvrelid, Lilja
%Y Xue, Nianwen
%Y Zhang, Yue
%S Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-286-2
%F swarup-etal-2025-syntax
%X Large Language Models (LLMs) have brought significant breakthroughs across all areas of Natural Language Processing (NLP), including Information Extraction (IE). However, knowledge gaps remain regarding their effectiveness in extracting entity-relation triplets, i.e. Joint Relation Extraction (JRE). JRE has been a key operation in creating knowledge bases that can be used to enhance Retrieval Augmented Generation (RAG) systems. Prior work highlights low-quality triplets generated by LLMs. Thus, this work investigates the impact of incorporating linguistic structures, such as constituency and dependency trees and semantic role labeling, to enhance the quality of the extracted triplets. The findings suggest that incorporating specific structural information enhances the uniqueness and topical relevance of the triplets, particularly in scenarios where multiple relationships are present.
%R 10.18653/v1/2025.xllm-1.5
%U https://aclanthology.org/2025.xllm-1.5/
%U https://doi.org/10.18653/v1/2025.xllm-1.5
%P 36-48
Markdown (Informal)
[From Syntax to Semantics: Evaluating the Impact of Linguistic Structures on LLM-Based Information Extraction](https://aclanthology.org/2025.xllm-1.5/) (Swarup et al., XLLM 2025)
ACL