Named Entity Recognition (NER) is a task of identifying named entities from text written in Natural Language. In this task, a string of text in the form of sentence or paragraph is accepted as input and relevant nouns like names of people, places, organizations etc. that are mentioned in that string are identified. This task belongs Information Extraction of the field of Natural Language Processing (NLP). Significant amount of work has been carried out on named entities recognition, but most of the researches have been done for resource-rich languages and domains. It is a challenging task for an informal text and code-mixed text which complicates the process with its unstructured and incomplete information. In this paper, we propose a method of extracting named entities from code-mixed data with different machine learning based algorithms using content and contextual features extracted from code-mixed data.

Named Entity, Machine Learning, Support Vector Machine, Decision Tree, K-Nearest Neighbour

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