Results 31 to 40 of about 745,152 (307)

Relation of the Relations: A New Paradigm of the Relation Extraction Problem

open access: yesCoRR, 2020
Passed the reviews of EMNLP; withdrawn for non-technical ...
Zhijing Jin 0001   +3 more
openaire   +2 more sources

An Open Relation Extraction Method for Domain Text Based on Hybrid Supervised Learning

open access: yesApplied 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
doaj   +1 more source

Portable extraction of partially structured facts from the web [PDF]

open access: yes, 2010
A novel fact extraction task is defined to fill a gap between current information retrieval and information extraction technologies. It is shown that it is possible to extract useful partially structured facts about different kinds of entities in a broad
Inguna Skadiņa   +8 more
core   +1 more source

DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT

open access: yesApplied 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
doaj   +1 more source

CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease Relation Extraction

open access: yesFrontiers 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
doaj   +1 more source

Neural Temporal Relation Extraction [PDF]

open access: yesProceedings 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
openaire   +1 more source

A relation-specific attention network for joint entity and relation extraction [PDF]

open access: yes, 2020
Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all relational triplets from plain texts. This is a big challenge due to some of the triplets extracted from one sentence may have
Song, Zeliang   +5 more
core   +1 more source

An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction

open access: yesInformation, 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
doaj   +1 more source

Latent Relational Model for Relation Extraction

open access: yes, 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
openaire   +2 more sources

Painless Relation Extraction with Kindred [PDF]

open access: yesBioNLP 2017, 2017
Relation extraction methods are essential for creating robust text mining tools to help researchers find useful knowledge in the vast published literature. Easy-to-use and generalizable methods are needed to encourage an ecosystem in which researchers can easily use shared resources and build upon each others’ methods.
Jake Lever, Steven J. Jones
openaire   +1 more source

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