Results 31 to 40 of about 104,722,032 (302)
An Open Relation Extraction Method for Domain Text Based on Hybrid Supervised Learning
Applied Sciences, 2023 Current research on knowledge graph construction is focused chiefly on general-purpose fields, whereas constructing knowledge graphs in vertically segmented professional fields faces numerous difficulties.
Xiaoxiong Wang, Jianpeng Hu
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Relation of the Relations: A New Paradigm of the Relation Extraction Problem
CoRR, 2020 Passed the reviews of EMNLP; withdrawn for non-technical ...
Zhijing Jin 0001 +3 more
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DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT
Applied Sciences, 2020 Along with studies on artificial intelligence technology, research is also being carried out actively in the field of natural language processing to understand and process people’s language, in other words, natural language.
SungMin Yang, SoYeop Yoo, OkRan Jeong
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CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease Relation Extraction
Frontiers in Genetics, 2021 Automatic extraction of chemical-induced disease (CID) relation from unstructured text is of essential importance for disease treatment and drug development.
Daojian Zeng, Chao Zhao, Zhe Quan
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Neural relation extraction: a survey
CoRR, 2020 Neural relation extraction discovers semantic relations between entities from unstructured text using deep learning methods. In this study, we present a comprehensive review of methods on neural network based relation extraction. We discuss advantageous and incompetent sides of existing studies and investigate additional research directions and ...
Mehmet Aydar, Ozge Bozal, Furkan Özbay
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Neural Temporal Relation Extraction [PDF]
Proceedings of the 15th Conference of the European Chapter of the
Association for Computational Linguistics: Volume 2, Short Papers, 2017 We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-of-the-art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that encoding relation arguments with ...
Dmitriy Dligach +4 more
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An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction
Information, 2020 Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to
Xiaoyu Han +3 more
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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 +3 more
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The impact of enriched linguistic annotation on the performance of extracting relation triples
, 2004 A relation extraction system recognises pre-defined relation types between two identified entities from natural language documents. It is important for a task of automatically locating missing instances in knowledge base where the instance is represented
Lewis, Paul +5 more
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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 +6 more
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