TMIANE: Design of an efficient Transformer-Based Model for Identification & Feature Analysis of Named Entities via Ensemble Operations

Abstract

Named Entity Recognition (NER) is a critical task in natural language processing that involves identifying and categorizing named entities in text. These entities can include people, organizations, locations, and more. Extracting features from these entities is equally important as it enables downstream tasks like sentiment analysis, text classification, and more. In this research paper, we propose an efficient Transformer-based model for identifying named entities and analyzing their features through ensemble operations. Our proposed model leverages the power of Transformer models such as BERT and XLNet for identifying named entities. We then convert the identified entities into feature vector sets using a combination of BERT and XLNet. These features are classified using the GoogLeNet convolutional neural network for model validation operations. By combining these different models through ensemble operations, we aim to improve the accuracy, precision, recall, and delay of the model for different use cases. The need for such a model arises due to the limitations of existing models for named entity recognition and feature analysis. While these models have achieved significant success, they still suffer from low accuracy, precision, recall, and high delay. Our proposed model overcomes these limitations by using ensemble operations to combine the strengths of different models. We compare the performance of our proposed model with existing models on standard datasets and show that it outperforms these models in terms of accuracy, precision, recall, and delay. Our results demonstrate the potential of ensemble operations in developing efficient models for named entity recognition and feature analysis. Overall, this research paper contributes to the development of more accurate and efficient models for natural language processing tasks.