SCIRP Mobile Website
Paper Submission

Why Us? >>

  • - Open Access
  • - Peer-reviewed
  • - Rapid publication
  • - Lifetime hosting
  • - Free indexing service
  • - Free promotion service
  • - More citations
  • - Search engine friendly

Free SCIRP Newsletters>>

Add your e-mail address to receive free newsletters from SCIRP.


Contact Us >>

WhatsApp  +86 18163351462(WhatsApp)
Paper Publishing WeChat
Book Publishing WeChat

Article citations


Shahi, T.B., (2012) Support Vector Machine Based POS Tagging for Nepali Text. Masters Dissertation, Central Department of Computer Science and IT, Tribhuvan University, Kirtipur.

has been cited by the following article:

  • TITLE: Named Entity Recognition for Nepali Text Using Support Vector Machines

    AUTHORS: Surya Bahadur Bam, Tej Bahadur Shahi

    KEYWORDS: Support Vector Machine; Named Entity Recognition; Machine Learning; Classification; Nepali Language Text

    JOURNAL NAME: Intelligent Information Management, Vol.6 No.2, March 17, 2014

    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. There has been growing interest in this field of research since the early 1990s. Named Entity Recognition has a vital role in different fields of natural language processing such as Machine Translation, Information Extraction, Question Answering System and various other fields. In this paper, Named Entity Recognition for Nepali text, based on the Support Vector Machine (SVM) is presented which is one of machine learning approaches for the classification task. A set of features are extracted from training data set. Accuracy and efficiency of SVM classifier are analyzed in three different sizes 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 uses a small dictionary which affects its performance. The learning performance of recognition system is observed. It is found that system can learn well from the small set of training data and increase the rate of learning on the increment of training size.