Abstract
Named entity recognition is a technique for extracting named entities from text and classifying them into various entity types. There has been a lot of research done on the Punjabi language’s Shahmukhi script, with less emphasis on the Gurmukhi script. This paper proposes a novel technique for extracting named entities from sentences written in the Punjabi language’s Gurmukhi script, which categorizes the entities into six different entity types. 15 k sentences from the Indic data corpus’ Punjabi data and various newspapers were used for this work, and they were annotated with Doccano, an open-source annotation tool. In addition, the researchers proposed and made public an annotated benchmark corpus for Gurmukhi script. The model was trained on the Spacy framework with only 12 k sentences selected at random from the Punjabi data corpus, and the results were validated with the remaining 3 k sentences in terms of F1-score, which was chosen as the evaluation metric. The experimental results have been analyzed, and the article contains useful information about the technique.
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Singh, N., Kumar, M., Singh, B. et al. DeepSpacy-NER: an efficient deep learning model for named entity recognition for Punjabi language. Evolving Systems 14, 673–683 (2023). https://doi.org/10.1007/s12530-022-09453-1
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DOI: https://doi.org/10.1007/s12530-022-09453-1