Urdu Named Entity Recognition and Classification System Using Artificial Neural Network

Abstract

Named Entity Recognition and Classification (NERC) is a process of identifying words and classifying them into person names, location names, organization names, and so on. In this article, we discuss the development of an Urdu Named Entity (NE) corpus, called the Kamran-PU-NE (KPU-NE) corpus, for three entity types, that is, Person, Organization, and Location, and marking the remaining tokens as Others (O). We use two supervised learning algorithms, Hidden Markov Model (HMM) and Artificial Neural Network (ANN), for the development of the Urdu NERC system. We annotate the 652852-token corpus taken from 15 different genres with a total of 44480 NEs. The inter-annotator agreement between the two annotators in terms of Kappa k statistic is 73.41%. With HMM, the highest recorded precision, recall, and f-measure values are 55.98%, 83.11%, and 66.90%, respectively, and with ANN, they are 81.05%, 87.54%, and 84.17%, respectively.