Retrieval and Sorting of Scientific Documents Based on Stacked Embedding and Hybrid Attention Model
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AFJ9LD8EX" target="_blank" >RIV/00216208:11320/25:FJ9LD8EX - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205027088&doi=10.1109%2fIJCNN60899.2024.10650167&partnerID=40&md5=3e8135cb0ef64c463c11d5477335a0d4" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205027088&doi=10.1109%2fIJCNN60899.2024.10650167&partnerID=40&md5=3e8135cb0ef64c463c11d5477335a0d4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN60899.2024.10650167" target="_blank" >10.1109/IJCNN60899.2024.10650167</a>
Alternative languages
Result language
angličtina
Original language name
Retrieval and Sorting of Scientific Documents Based on Stacked Embedding and Hybrid Attention Model
Original language description
Making full use of mathematical formulas and their contextual information is crucial for enhancing the performance of scientific literature retrieval models, where mathematical formulas serve as core elements. The existing methods inadequately use formula structure and contextual information in situations involving mathematical formulas, and ignore the part-of-speech features contained in the context. A two stage scientific document retrieval method, based on stacked embedding and hybrid attention fusion part-of-speech features, was proposed in this paper. Initially, MathML in documents is used to learn the structural and semantic information of mathematical formulas, facilitating scientific document retrieval focused on mathematical expression. Subsequently, the document context is extracted, the context's part-of-speech features are introduced into the model through stacked embeddings, and a hybrid attention mechanism is used to learn the dependency between part-of-speech and context, features are then generated to improve the rationality of retrieval result ranking. Experiments were performed on the NTCIR-12 dataset in which we expanded with Chinese literature. The mAP@10 is 0.865 and NDCG@10 is 0.863 respectively. © 2024 IEEE.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proc Int Jt Conf Neural Networks
ISBN
979-835035931-2
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
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Event location
Yokohama
Event date
Jan 1, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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