NAMED ENTITY RECOGNITION TOOLS AND TECHNIQUES IN CUSTOM NATURAL LANGUAGE PROCESSING MODELS

Abstract

Named Entity Recognition automatically extracts crucial information like names, locations, and organizations from unstructured text data. This research provides a comparative analysis of prominent NLP libraries, including SpaCy, Apache OpenNLP, and TensorFlow, for building custom NER models. Performance is evaluated based on key metrics such as accuracy, F-score, prediction time, model size, and training efficiency. Using a consistent dataset, SpaCy consistently outperforms other libraries in terms of accuracy. The study also explores various NER techniques, encompassing rule-based, learning-based, and hybrid approaches, highlighting their applications in diverse domains. Furthermore, it examines the strengths and weaknesses of different libraries and their associated tools across Java, Python, and Cython programming languages. Factors such as model size, prediction time, training loss, and F-measure are considered in the comparison. The results consistently demonstrate SpaCy’s superior performance and accuracy compared to other models. This makes SpaCy a valuable tool for information extraction from the ever-increasing volume of textual data available online, in social media, and other sources. The study contributes to a better understanding of NER techniques and tools, aiding researchers and developers in selecting the most appropriate approach for their specific needs. The findings emphasize the importance of choosing the right tools and techniques for efficient and accurate information extraction.