Paper Key : IRJ************040
Author: Chakradhar Reddy
Date Published: 17 Apr 2024
Abstract
Named Entity Recognition (NER) holds paramount importance in the realm of natural language processing, especially within the context of journal articles. This paper conducts an exhaustive analysis of supervised learning approaches employed for NER in journal articles, highlighting the nuances of evaluation methodologies and the criticality of pivotal performance metrics. The research scrutinizes evaluation metrics such as precision, recall, and the F1-score, elucidating their role in assessing model accuracy at both token and entity granularity levels. Additionally, the study delves into the deployment of cross-validation techniques, vital for bolstering the resilience and generalizability of NER models across heterogeneous datasets. The paper accentuates the significance of juxtaposing results against baseline models to discern the efficacy and pinpoint areas ripe for enhancement within supervised learning paradigms. Error analysis is identified as an instrumental phase, enabling the detection of recurrent error patterns and guiding targeted model optimizations. Furthermore, the paper underscores the imperative of assessing model generalizability to novel data, illuminating the pragmatic viability of supervised learning techniques in real-world scenarios. By providing a comprehensive overview of evaluation methodologies and performance considerations pertinent to NER in journal articles, this review aspires to arm researchers and professionals with invaluable insights, catalyzing advancements in both theoretical natural language processing research and its tangible applications.