A Named Entity Recognition System for Malayalam using Neural Networks
A P Ajees, Sumam Mary Idicula · Procedia Computer Science · 2018
Named Entity Recognition is the process of identifying the entities in the text document and categorizing them into predefined categories such as Person, Location, Organisation, etc. It is an important step in the processing of natural language text. Named entity recognition systems aim at extracting relevant information from the text. Various methods are applied for NER in Malayalam. In this paper, we propose an NER system for Malayalam using neural networks. Neural networks are exceptionally powerful tools for learning representations of data with multiple levels of abstraction. The proposed system utilizes different features such as POS information of the word, embedded representation of words and suffixes, POS information of preceding words, etc. We have used a corpus of 20615 sentences for training and testing. With less number of features, the system was able to obtain the state of the art performance in NER for Malayalam.