Entity Extraction from Social Media Text Indian Languages (ESM-IL)

This paper shows the implementation of named entity recognition (NER) which is one of the applications of Natural Language Processing and is regarded as the subtask of information retrieval. NER is the process to detect Named Entities (NEs) in a document and to categorize them into certain Named entity classes such as the name of organization, person, location, sport, river, city, country, quantity etc. There are lots of work have been accomplished in English related to NER. But, at present, still we have not been able to achieve much of the success pertaining to NER in the Indian languages. The following paper discusses about NER, the various approaches of NER, Performance Metrics, the challenges in NER in the Indian languages and finally some of the results that have been achieved by performing NER in Hindi by aggregating approaches such as Rule based CRF suite and for tagging RDRpostagger and geniatagger. The paper shows working methodology and its result on named entity extraction from social media text of fire 2015. CCS Concepts • Theory of computation~Support vector machines • Computing methodologies~Natural language processing • Information systems~Information extraction • Human-centered computing~Social tagging systems