Results 31 to 40 of about 1,302,829 (277)

Document-Level Relation Extraction with Relation Correlation Enhancement

, 2023
Document-level relation extraction (DocRE) is a task that focuses on identifying relations between entities within a document. However, existing DocRE models often overlook the correlation between relations and lack a quantitative analysis of relation correlations.
Huang, Yusheng, Lin, Zhouhan
openaire   +2 more sources

Extracting Relations Between Sectors

2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), 2022
The term "sector" in professional business life is a vague concept since companies tend to identify themselves as operating in multiple sectors simultaneously. This ambiguity poses problems in recommending jobs to job seekers or finding suitable candidates for open positions.
Daniş, Fahri Serhan, Turhan, Sultan Nezihe, Orman, Gunce Keziban, Kara, Atakan   +3 more
openaire   +3 more sources

Dialogue-Based Relation Extraction [PDF]

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020
To appear in ACL ...
Yu, Dian, Sun, Kai, Cardie, Claire, Yu, Dong   +3 more
openaire   +2 more sources

Research on the Chinese Named-Entity–Relation-Extraction Method for Crop Diseases Based on BERT

Agronomy, 2022
In order to integrate fragmented text data of crop disease knowledge to solve the current problems of disordered knowledge management, weak correlation and difficulty in knowledge sharing, a Chinese named-entity–relation-extraction model for crop ...
Wenhao Zhang, Chunshan Wang, Huarui Wu, Chunjiang Zhao, Guifa Teng, Sufang Huang, Zhen Liu   +6 more
doaj   +1 more source

Using Neural Networks for Relation Extraction from Biomedical Literature

, 2020
Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems.
A Koike, A Lamurias, A Lamurias, A Lamurias, A Lamurias, A Singhal, AV Aho, B Xu, CD Manning, CH Alves, D Westergaard, D Zhou, E Guresen, F Rinaldi, HC Wang, HM Müller, J Hastings, L Aroyo, M Ashburner, MY Kim, N Ma, N Peng, P Goyal, P Zweigenbaum, PN Robinson, Q Li, QL Nguyen, S HayKin, S Hochreiter, TR Gruber, W Wang, WWM Fleuren, Y Hao, Y Luo, Y Xu   +34 more
core   +1 more source

Application of Public Knowledge Discovery Tool (PKDE4J) to Represent Biomedical Scientific Knowledge

Frontiers in Research Metrics and Analytics, 2018
In today’s era of information explosion, extracting entities and their relations in large-scale, unstructured collections of text to better represent knowledge has emerged as a daunting challenge in biomedical text mining.
Min Song, Munui Kim, Keunyoung Kang, Yong Hwan Kim, Sieun Jeon   +4 more
doaj   +1 more source

Graph Adaptation Network with Domain-Specific Word Alignment for Cross-Domain Relation Extraction

Sensors, 2020
Cross-domain relation extraction has become an essential approach when target domain lacking labeled data. Most existing works adapted relation extraction models from the source domain to target domain through aligning sequential features, but failed to ...
Zhe Wang, Bo Yan, Chunhua Wu, Bin Wu, Xiujuan Wang, Kangfeng Zheng   +5 more
doaj   +1 more source

Latent Relational Model for Relation Extraction

, 2019
Analogy is a fundamental component of the way we think and process thought. Solving a word analogy problem, such as mason is to stone as carpenter is to wood, requires capabilities in recognizing the implicit relations between the two word pairs. In this paper, we describe the analogy problem from a computational linguistics point of view and explore ...
Gaetano Rossiello, Alfio Gliozzo, Nicolas Fauceglia, Giovanni Semeraro   +3 more
openaire   +2 more sources

A Machine Learning Filter for the Slot Filling Task

Information, 2018
Slot Filling, a subtask of Relation Extraction, represents a key aspect for building structured knowledge bases usable for semantic-based information retrieval.
Kevin Lange Di Cesare, Amal Zouaq, Michel Gagnon, Ludovic Jean-Louis   +3 more
doaj   +1 more source

Semantic Enhanced Distantly Supervised Relation Extraction via Graph Attention Network

Information, 2020
Distantly Supervised relation extraction methods can automatically extract the relation between entity pairs, which are essential for the construction of a knowledge graph.
Xiaoye Ouyang, Shudong Chen, Rong Wang
doaj   +1 more source