Results 1 to 10 of about 5,797,510 (205)
A survey on Relation Extraction
Intelligent Systems with Applications, 2023 With the advent of the Internet, the daily production of digital text in the form of social media, emails, blogs, news items, books, research papers, and Q&A forums has increased significantly.
Kartik Detroja +2 more
doaj +3 more sources
Global Relation Embedding for Relation Extraction [PDF]
Proceedings of the 2018 Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language
Technologies, Volume 1 (Long Papers), 2018 We study the problem of textual relation embedding with distant supervision. To combat the wrong labeling problem of distant supervision, we propose to embed textual relations with global statistics of relations, i.e., the co-occurrence statistics of ...
Gur, Izzeddin +5 more
core +2 more sources
Revisiting Unsupervised Relation Extraction [PDF]
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020 Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs).
Ananiadou, Sophia +2 more
core +2 more sources
Model tuning or prompt Tuning? a study of large language models for clinical concept and relation extraction. [PDF]
J Biomed Inform, 2023 OBJECTIVE To develop soft prompt-based learning architecture for large language models (LLMs), examine prompt-tuning using frozen/unfrozen LLMs, and assess their abilities in transfer learning and few-shot learning.
Peng C +6 more
europepmc +3 more sources
Document-level Relation Extraction with Relation Correlations
Neural Networks, 2023 Document-level relation extraction faces two often overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and introduce it into the document ...
Ridong Han +5 more
openaire +3 more sources
Label-Guided relation prototype generation for Continual Relation Extraction [PDF]
PeerJ Computer ScienceContinual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data. To address the problem of catastrophic forgetting, some existing research endeavors have focused on exploring memory replay methods by
Shuang Liu +3 more
doaj +3 more sources
Enhancing biomedical relation extraction with directionality. [PDF]
BioinformaticsAbstract Summary Biological relation networks contain rich information for understanding the biological mechanisms behind the relationship of entities such as genes, proteins, diseases, and chemicals.
Lai PT, Wei CH, Tian S, Leaman R, Lu Z.
europepmc +3 more sources
Prompt Tuning in Biomedical Relation Extraction. [PDF]
J Healthc Inform ResHe J +10 more
europepmc +2 more sources
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction [PDF]
The Web Conference, 2021 Recently, prompt-tuning has achieved promising results for specific few-shot classification tasks. The core idea of prompt-tuning is to insert text pieces (i.e., templates) into the input and transform a classification task into a masked language ...
Xiang Chen +9 more
semanticscholar +1 more source
Multi-Attribute Relation Extraction (MARE): Simplifying the Application of Relation Extraction [PDF]
Proceedings of the 2nd International Conference on Deep Learning Theory and Applications, 2021 Preprint of short paper for the 2nd International Conference on Deep Learning Theory and Applications (2021)
Klöser, Lars +3 more
openaire +2 more sources