An Automated System for Tamil Named Entity Recognition Using Hybrid Approach

Abstract

Named Entity Recognition is the process of identifying and recognizing named entities such as person, organization, location, date, time and money in the text documents. Named Entity Recognition is a subtask of Information Extraction. Information Extraction is the process of extracting the relevant data from documents. It is one of the research areas in Natural language processing. In this project implement a named entity recognizer using the hybrid approach that uses both Rule based and Hidden Markov Model in succession, which identifies only person, location and organization names respectively. Input data for proposed Named Entity Recognition system is any text document related to the any domain but limited size corpora respectively in Tamil language. In this system are tagging each word by using POS tagger and then imposing certain rules such as Lexical features and use some Gazetteers. HMM model using E-M algorithm is taken output data from trained as input to recognition system. The main purpose of this system identifies unknown entities and solves the problem of same name entity in different positions in the same document. The system is measuring the recall and precision parameters calculate the F-measure score. Goal of this project is to improve the performance of NER system to achieving high F-measure score.

Named Entity Recognition of Kumauni Language using Machine Learning (ML)

“…An example of an NLP application is Named Entity Recognition (NER). It is also a subtask of many NLP applications such as information retrieval, machine translation, question answering, extraction, and text summarization [5]. Take this statement as an example: On August 7th, 2014, Ramu enrolled at Amrita in Coimbatore to pursue his master's degree.…”