Enhancing the Performance of Telugu Named Entity Recognition Using Gazetteer Features

• Main Authors: SaiKiranmai Gorla, Lalita Bhanu Murthy Neti, Aruna Malapati
• Format: Article
• Language: English
• Published: MDPI AG 2020-02-01
• Series: Information
• Subjects: information extraction; named entity recognition; telugu language; gazetteer; support vector machine; conditional random field; margin infused relaxed algorithm
• Online Access: https://www.mdpi.com/2078-2489/11/2/82

Named entity recognition (NER) is a fundamental step for many natural language processing tasks and hence enhancing the performance of NER models is always appreciated. With limited resources being available, NER for South-East Asian languages like Telugu is quite a challenging problem. This paper attempts to improve the NER performance for Telugu using gazetteer-related features, which are automatically generated using Wikipedia pages. We make use of these gazetteer features along with other well-known features like contextual, word-level, and corpus features to build NER models. NER models are developed using three well-known classifiers—conditional random field (CRF), support vector machine (SVM), and margin infused relaxed algorithms (MIRA). The gazetteer features are shown to improve the performance, and theMIRA-based NER model fared better than its counterparts SVM and CRF.