Named Entity Recognition for Nepali Text using Support Vector Machine

Abstract

Named Entity Recognition aims to identify and to classify rigid designators in text such as proper names, biological species, and temporal expressions into some predefined categories. It resolves who, where and how much problems in information extraction and leads to the resolution of the what and how problems in further processing. There has been growing interest in this field of research since the early 1990s. Named Entity Recognition have vital role in different field of natural language processing such as Machine Translation, Information Extraction, Question Answering system and various other fields. In this thesis, Named Entity Recognition for Nepali Text, based on the support vector machine is present which is one of the machine is learning approaches and domain independent work. A set of features are extracted from training data set. Accuracy and efficiency of SVM classifier is analyzed in three different size of training data set. Recognition systems are tested with ten datasets for Nepali text. The strength of this work is the efficient feature extraction and the comprehensive recognition techniques. The support vector machine based named entity recognition is limited to use a certain set of features and it use a small dictionary which affects its performance. The learning performance of recognition system is observed and found that it can learn well from the small set of training data and increases the rate of learning on the increment of training size.