@inproceedings{jain-etal-2024-knowledge,
title = "Knowledge-Driven Cross-Document Relation Extraction",
author = "Jain, Monika and
Mutharaju, Raghava and
Singh, Kuldeep and
Kavuluru, Ramakanth",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.227",
pages = "3787--3797",
abstract = "Relation extraction (RE) is a well-known NLP application often treated as a sentence or document-level task. However, a handful of recent efforts explore it across documents or in the cross-document setting (CrossDocRE). This is distinct from the single document case because different documents often focus on disparate themes, while text within a document tends to have a single goal.Current CrossDocRE efforts do not consider domain knowledge, which are often assumed to be known to the reader when documents are authored. Here, we propose a novel approach, KXDocRE, that embed domain knowledge of entities with input text for cross-document RE. Our proposed framework has three main benefits over baselines: 1) it incorporates domain knowledge of entities along with documents{'} text; 2) it offers interpretability by producing explanatory text for predicted relations between entities 3) it improves performance over the prior methods. Code and models are available at \url{https://github.com/kracr/cross-doc-relation-extraction}.",
}
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<abstract>Relation extraction (RE) is a well-known NLP application often treated as a sentence or document-level task. However, a handful of recent efforts explore it across documents or in the cross-document setting (CrossDocRE). This is distinct from the single document case because different documents often focus on disparate themes, while text within a document tends to have a single goal.Current CrossDocRE efforts do not consider domain knowledge, which are often assumed to be known to the reader when documents are authored. Here, we propose a novel approach, KXDocRE, that embed domain knowledge of entities with input text for cross-document RE. Our proposed framework has three main benefits over baselines: 1) it incorporates domain knowledge of entities along with documents’ text; 2) it offers interpretability by producing explanatory text for predicted relations between entities 3) it improves performance over the prior methods. Code and models are available at https://github.com/kracr/cross-doc-relation-extraction.</abstract>
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%0 Conference Proceedings
%T Knowledge-Driven Cross-Document Relation Extraction
%A Jain, Monika
%A Mutharaju, Raghava
%A Singh, Kuldeep
%A Kavuluru, Ramakanth
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F jain-etal-2024-knowledge
%X Relation extraction (RE) is a well-known NLP application often treated as a sentence or document-level task. However, a handful of recent efforts explore it across documents or in the cross-document setting (CrossDocRE). This is distinct from the single document case because different documents often focus on disparate themes, while text within a document tends to have a single goal.Current CrossDocRE efforts do not consider domain knowledge, which are often assumed to be known to the reader when documents are authored. Here, we propose a novel approach, KXDocRE, that embed domain knowledge of entities with input text for cross-document RE. Our proposed framework has three main benefits over baselines: 1) it incorporates domain knowledge of entities along with documents’ text; 2) it offers interpretability by producing explanatory text for predicted relations between entities 3) it improves performance over the prior methods. Code and models are available at https://github.com/kracr/cross-doc-relation-extraction.
%U https://aclanthology.org/2024.findings-acl.227
%P 3787-3797
Markdown (Informal)
[Knowledge-Driven Cross-Document Relation Extraction](https://aclanthology.org/2024.findings-acl.227) (Jain et al., Findings 2024)
ACL
- Monika Jain, Raghava Mutharaju, Kuldeep Singh, and Ramakanth Kavuluru. 2024. Knowledge-Driven Cross-Document Relation Extraction. In Findings of the Association for Computational Linguistics ACL 2024, pages 3787–3797, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.