Named Entity Recognition - Techniques and Evaluation: Investigating techniques and evaluation methods for named entity recognition (NER) systems to identify and classify entities in text data

Abstract

Named Entity Recognition (NER) is a crucial task in natural language processing (NLP) that involves identifying and classifying named entities in text data. This paper provides a comprehensive review of various techniques used in NER systems, along with an evaluation of their effectiveness. We discuss popular approaches such as rule-based systems, machine learning models, and deep learning architectures, highlighting their strengths and weaknesses. Furthermore, we examine evaluation metrics and datasets commonly used to assess the performance of NER systems. By analyzing the current state-of-the-art in NER, this paper aims to provide insights into future research directions and improvements in NER systems.