Results 11 to 20 of about 5,797,629 (324)
Revisiting Relation Extraction in the era of Large Language Models [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text. Standard supervised RE techniques entail training modules to tag tokens comprising entity spans and then predict the relationship between them ...
Somin Wadhwa +2 more
semanticscholar +1 more source
GPT-RE: In-context Learning for Relation Extraction using Large Language Models [PDF]
Conference on Empirical Methods in Natural Language Processing, 2023 In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e.g., GPT-3), they still lag significantly behind fully-supervised baselines (e.g., fine-tuned BERT) in relation extraction (RE).
Zhen Wan +6 more
semanticscholar +1 more source
Biomedical Relationship Extraction Method Based on Prompt Learning [PDF]
Jisuanji kexue, 2023 Extracting the relationship between entities from unstructured biomedical text data is of great significance for the development of biomedical informatization.At the same time,it is also a research hotspot in the field of natural language processing.At ...
WEN Kunjian, CHEN Yanping, HUANG Ruizhang, QIN Yongbin
doaj +1 more source
A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers [PDF]
ACM Computing Surveys, 2023 Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and ...
Xiaoyan Zhao +8 more
semanticscholar +1 more source
Zero-shot Temporal Relation Extraction with ChatGPT [PDF]
Workshop on Biomedical Natural Language Processing, 2023 The goal of temporal relation extraction is to infer the temporal relation between two events in the document. Supervised models are dominant in this task. In this work, we investigate ChatGPT’s ability on zero-shot temporal relation extraction.
Chenhan Yuan, Qianqian Xie, S. Ananiadou
semanticscholar +1 more source
DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction [PDF]
Conference of the European Chapter of the Association for Computational Linguistics, 2023 Document-level relation extraction (DocRE) is the task of identifying all relations between each entity pair in a document. Evidence, defined as sentences containing clues for the relationship between an entity pair, has been shown to help DocRE systems ...
Youmi Ma, An Wang, Naoaki Okazaki
semanticscholar +1 more source
Joint extraction of entities and relations by entity role recognition
Cognitive Robotics, 2022 Joint extracting entities and relations from unstructured text is a fundamental task in information extraction and a key step in constructing large knowledge graphs, entities and relations are constructed as relational triples of the form (subject ...
Xi Han, Qi-Ming Liu
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Document-level Relation Extraction as Semantic Segmentation [PDF]
International Joint Conference on Artificial Intelligence, 2021 Document-level relation extraction aims to extract relations among multiple entity pairs from a document. Previously proposed graph-based or transformer-based models utilize the entities independently, regardless of global information among relational ...
Ningyu Zhang +8 more
semanticscholar +1 more source
How to Unleash the Power of Large Language Models for Few-shot Relation Extraction? [PDF]
SUSTAINLP, 2023 Scaling language models have revolutionized widespread NLP tasks, yet little comprehensively explored few-shot relation extraction with large language models. In this paper, we investigate principal methodologies, in-context learning and data generation,
Xin Xu +3 more
semanticscholar +1 more source
Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction [PDF]
AAAI Conference on Artificial Intelligence, 2021 Entities, as the essential elements in relation extraction tasks, exhibit certain structure. In this work, we formulate such entity structure as distinctive dependencies between mention pairs.
Benfeng Xu +4 more
semanticscholar +1 more source