CR-M-SpanBERT: Multiple embedding-based DNN coreference resolution using self-attention SpanBERT

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AHZD7NCW6" target="_blank" >RIV/00216208:11320/25:HZD7NCW6 - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186114766&doi=10.4218%2fetrij.2023-0308&partnerID=40&md5=25e606202bf4fd7c289e32d5a26c8827" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186114766&doi=10.4218%2fetrij.2023-0308&partnerID=40&md5=25e606202bf4fd7c289e32d5a26c8827</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4218/etrij.2023-0308" target="_blank" >10.4218/etrij.2023-0308</a>

Result language
angličtina
Original language name
CR-M-SpanBERT: Multiple embedding-based DNN coreference resolution using self-attention SpanBERT
Original language description
This study introduces CR-M-SpanBERT, a coreference resolution (CR) model that utilizes multiple embedding-based span bidirectional encoder representations from transformers, for antecedent recognition in natural language (NL) text. Information extraction studies aimed to extract knowledge from NL text autonomously and cost-effectively. However, the extracted information may not represent knowledge accurately owing to the presence of ambiguous entities. Therefore, we propose a CR model that identifies mentions referring to the same entity in NL text. In the case of CR, it is necessary to understand both the syntax and semantics of the NL text simultaneously. Therefore, multiple embeddings are generated for CR, which can include syntactic and semantic information for each word. We evaluate the effectiveness of CR-M-SpanBERT by comparing it to a model that uses SpanBERT as the language model in CR studies. The results demonstrate that our proposed deep neural network model achieves high-recognition accuracy for extracting antecedents from NL text. Additionally, it requires fewer epochs to achieve an average F1 accuracy greater than 75% compared with the conventional SpanBERT approach. 1225-6463/$ © 2024 ETRI.
Czech name
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Czech description
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Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
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)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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