Bengali Named Entity Recognition Using Support Vector Machine

Named Entity Recognition (NER) aims to classify each word of a document into predefined target named entity classes and is nowadays considered to be fundamental for many Natural Language Processing (NLP) tasks such as information retrieval, machine translation, information extraction, question answering systems and others. This paper reports about the development of a NER system for Bengali using Support Vector Machine (SVM). Though this state of the art machine learning method has been widely applied to NER in several well-studied languages, this is our first attempt to use this method to Indian languages (ILs) and particularly for Bengali. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the various named entity (NE) classes. A portion of a partially NE tagged Bengali news corpus, developed from the archive of a leading Bengali newspaper available in the web, has been used to develop the SVM-based NER system. The training set consists of approximately 150K words and has been manually annotated with the sixteen NE tags. Experimental results of the 10-fold cross validation test show the effectiveness of the proposed SVM based NER system with the overall average Recall, Precision and F-Score of 94.3%, 89.4% and 91.8%, respectively. It has been shown that this system outperforms other existing Bengali NER systems.