• Ganta Christina
• Dr. Selvani Deepthi Kavila
• Badda Sravani
• Boddu Sri Sankara Avinash

In the field of Natural Language Processing (NLP), Named Entity Recognition (NER) is one of the major task. The main challenge in this extraction is to extract Entities that lies in the inadequate information available in a tweet. There has been plenty of work done on this domain of entity extraction but it was mainly focused on popular languages such as English. In general extraction of entities from an informal text makes it difficult and for data that is written in two or more languages (code-mixed) makes it more difficult. In this paper the author has proposed the Machine Learning algorithms like Decision tree, and Conditional Random Field (CRF) with efficiencies of 60% and 76% respectively. The dataset was collected from FIRE-2016.

Social media text, Entity extraction, Code-mixed data, CRF, BIO format, Decision Tree

"A NOVEL APPROACH FOR ENTITY EXTRACTION IN CODE MIXED DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.271-278, April-2019, Available :http://www.jetir.org/papers/JETIRBF06053.pdf

"A NOVEL APPROACH FOR ENTITY EXTRACTION IN CODE MIXED DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp271-278, April-2019, Available at : http://www.jetir.org/papers/JETIRBF06053.pdf