Named Entity Recognition (NER) in NLP Techniques, Tools Accuracy and Performance.

Main Article Content

Salman Naseer
Muhammad Mudasar Ghafoor
Sohaib bin Khalid Alvi
Anam Kiran
Shafique Ur Rahmand, Ghulam Murtazae
Ghulam Murtaza

Abstract

A huge amount of textual information is available on Web, Facebook, blogs and Wikipedia, everyday rising new techniques, algorithms and tools extract the useful information. Therefore, Named Entity Recognition (NER) is very important technique to recognize the noun entities like such as names, date or time, location, medicine names etc. Many researchers have proposed many techniques in different languages and domains for extract information from text that techniques are help to developed new NER applications. Here, we discuss NER techniques: rule-based, learning-based and hybrid approaches and their application and systems. We also present advantages and disadvantages of different libraries and their tools using Java, Python, and Cython programming languages which are SpaCy, Apache OpenNLP, StanfordNLP and tensorflow. Few libraries served a NER pre-built models that we use for comparison. We compare these few libraries on the basedon training accuracy, model size, time prediction, training loss data and F-measure. The data set is same for all libraries during training and testing, Spacy library provided a higher performance accuracy and good results as compare the other models.

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How to Cite
Naseer, S., Mudasar Ghafoor, M., Alvi, S. bin K., Kiran, A., Shafique Ur Rahmand, Ghulam Murtazae, & Murtaza, G. (2022). Named Entity Recognition (NER) in NLP Techniques, Tools Accuracy and Performance. Pakistan Journal of Multidisciplinary Research, 2(2), 293-308. Retrieved from http://pjmr.org/pjmr/article/view/150
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Articles

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