Hybrid Model for Named Entity Recognition

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.