Welcome to the InfoSci Platform
Hybrid Model for Named Entity Recognition

Hybrid Model for Named Entity Recognition

Nikhil Chaturvedi, Jigyasu Dubey
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 12
ISSN: 2637-7888|EISSN: 2637-7896|EISBN13: 9781683183471|DOI: 10.4018/IJDAI.311063
Cite Article Cite Article

MLA

Chaturvedi, Nikhil, and Jigyasu Dubey. "Hybrid Model for Named Entity Recognition." IJDAI vol.14, no.1 2022: pp.1-12. http://doi.org/10.4018/IJDAI.311063

APA

Chaturvedi, N. & Dubey, J. (2022). Hybrid Model for Named Entity Recognition. International Journal of Distributed Artificial Intelligence (IJDAI), 14(1), 1-12. http://doi.org/10.4018/IJDAI.311063

Chicago

Chaturvedi, Nikhil, and Jigyasu Dubey. "Hybrid Model for Named Entity Recognition," International Journal of Distributed Artificial Intelligence (IJDAI) 14, no.1: 1-12. http://doi.org/10.4018/IJDAI.311063

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Named entity recognition is an important factor that has a direct and significant impact on the quality of neural sequence labelling. It entails choosing encoding input data to create grammatical and semantic representation vectors. The main goal of this research is to provide a hybrid neural network model for a specific sequence labelling task such as named entity recognition. Three subnetworks are used in this hybrid model to ensure that information at the character, capitalization levels, and word-level contextual representation is fully utilized. The authors used different samples for training and development sets on the CoNLL-2003 dataset to show that the model could compare its performance to that of other state-of-the-art models.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.