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

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


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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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


Shah, D. N., and H. Bhadka. 2017. A survey on various approaches used in named entity recognition for Indian languages. International Journal of Computer Application 167 (1):11–18. doi:10.5120/ijca2017913878.

L.A.Pizzato ,D.Molla , C.Paris, Pseudo relevance feedback using named entities for question answering, in: Proceeding soft he 2006 Australian Language Technology Workshop, ALTW- 2006,2006,pp.89–90

Gürkan, A. T., B. Özenç, I. Çam, B. Avar, G. Ercan, and O. T. Y?ld?z. 2017. A new approachfor named entity recognition. 2nd international conference on computer science and engineering 474–79. doi: 10.1109/UBMK.2017.8093439

Ben Abacha, A., Zweigenbaum, P.: Medical entity recognition: a comparaison of semantic and statistical methods. In: Proceedings of BioNLP 2011 Workshop, pp. 56–64. Association for Computational Linguistics, Portland, June 2011. http://

Palshikar, G. K. (2013). Techniques for named entity recognition: A Survey. In Bioinformatics: Concepts, Methodologies, Tools, and Applications (pp. 400–426). 3604-0. ch022

N. Kanya, Dr. T. Ravi, “Modeling and Techniques in Named Entity Recognition – An Information Extraction Task”, Third International Conference on Sustainable Energy and Intelligent System (SEISCON 2012), Tamil Nadu, India, 27-29 December 2012.

Sazali, S. S., Rahman, N. A., & Bakar, Z. A. (2016).Information

extraction: Evaluating named entity recognition from classical

Malay documents. 2016 Third International Conference on

Information Retrieval and Knowledge Management

(CAMP). doi:10.1109/infrkm.2016.7806333

Goyal, A., Gupta, V., & Kumar, M. (2018).Recent Named Entity

Recognition and Classification techniques: A systematic review.

Computer Science Review, 29, 21–


Fok, W. W. T., He, Y. S., Yeung, H. H. A., Law, K. Y., Cheung, K., Ai,

Y., & Ho, P. (2018).Prediction model for students’ future development by deep learning and tensorflow artificial intelligence

engine. 2018 4th International Conference on Information

Management (ICIM). doi:10.1109/infoman.2018.8392818.

Furrer, L., Jancso, A., Colic, N., & Rinaldi, F. (2019). OGER++: hybrid

multi-type entity recognition. Journal of Cheminformatics,

(1). doi:10.1186/s13321-018-0326-3.

?niegula, A., Poniszewska-Mara?da, A., Chom?tek, ?.: Towards the

named entity recognition methods in biomedical field. In:

Chatzigeorgiou, A., et al. (eds.) SOFSEM 2020. LNCS, vol.

, pp. 375–387. Springer, Cham (2020).

Jiang, R., Banchs, R.E., Li, H.:(2020) Evaluating and Combining Name Entity Recognition System, pp. 21–27. Publisher : springer 2703/

Boag, W., Sergeeva, E., Kulshreshtha, S., Szolovits, P., Rumshisky, A., Naumann, T.: CliNER 2.0: Accessible and Accurate Clinical Concept Extraction. http://

Sintayehu, H., Lehal, G.S. Named entity recognition: a semi-supervised

learning approach. Int. j. inf. tecnol. (2020).

Dawar, K., Samuel, A. J., & Alvarado, R. (2019). Comparing Topic

Modeling and Named Entity Recognition Techniques for the

Semantic Indexing of a Landscape Architecture Textbook. 2019

Systems and Information Engineering Design Symposium

(SIEDS). doi:10.1109/sieds.2019.8735642

Shelar, H., Kaur, G., Heda, N., & Agrawal, P. (2020). Named Entity

Recognition Approaches and Their Comparison for Custom NER

Model. Science & Technology Libraries, 1–


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