Hybrid Model for Named Entity Recognition

Hybrid Model for Named Entity Recognition

Nikhil Chaturvedi, Jigyasu Dubey
Copyright: © 2022 |Pages: 12
DOI: 10.4018/IJDAI.311063
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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.
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Several researchers have contributed significantly to the field of named entity recognition by inventing numerous ways of extracting sequence tagging from English text.

(Sun et al., 2019) highlight how previous scholars surveyed named entity recognitions in the statistical machine learning period, although NER task has evolved significantly in the last decade. On the one hand, transfer learning, deep learning, knowledge bases, and other methodologies are increasingly being used in NER systems. To demonstrate how these changes have occurred, they present an overview of NER based on 162 articles published at NLP-related conferences between 1996 and 2017.

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