Named Entity Recognition (NER) plays a pivotal role in automating the extraction and categorization of named entities from textual data, enabling efficient information retrieval and analysis across various domains. This paper presents a comprehensive study on NER techniques, focusing particularly on their application in news articles.The project employs Conditional Random Fields (CRF) as a discriminative probabilistic model for sequence labeling tasks, leveraging feature engineering and preprocessing steps for accurate entity recognition.The CoNLL-2003 dataset serves as the benchmark dataset for training and evaluating the CRF model, showcasing its performance in identifying entities such as persons, organizations, and locations.
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Chavan et al. (2024) studied this question.
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