Extracting fact-condition relation from geological papers via deep structured semantic model with multi-grained representation-Reference-Cited by-同舟云学术

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Extracting fact-condition relation from geological papers via deep structured semantic model with multi-grained representation

Published:2023-09 Issue: Volume:178 Page:105416
ISSN:0098-3004
Container-title:Computers & Geosciences
language:en
Short-container-title:Computers & Geosciences

Author:

Chen Qizhi^ORCID,Yao Hong^ORCID,Zhou Diange,Li Shengwen,Dong Lijun

Funder

Hubei Key Laboratory of Intelligent Geo-Information Processing

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Information Systems

Reference29 articles.

1. Construction of the literature graph in semantic scholar;Ammar,2018

2. SciBERT: a pretrained language model for scientific text;Beltagy,2019

3. A collaboratively created graph database for structuring human knowledge;Bollacker,2008

4. Fact-condition statements and super relation extraction for geothermic knowledge graphs construction;Chen;Geosci. Front.,2022

5. Pre-training of deep bidirectional transformers for language understanding;Devlin,2019

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1. Joint extraction of entity relations from geological reports based on a novel relation graph convolutional network;Computers & Geosciences;2024-05

2. Multi-granularity retrieval of mineral resource geological reports based on multi-feature association;Ore Geology Reviews;2024-02

3. Semantic information extraction and search of mineral exploration data using text mining and deep learning methods;Ore Geology Reviews;2024-02

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