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falak khursheed
Mohd.Shahid Hussain


Social media has become very popular communication tool among internet users in the recent years. A large unstructured data is available for analysis on the social web. The data available on these sites have redundancies as users are free to enter the data according to their knowledge and interest. This data needs to be normalized before doing any analysis due to the presence of various redundancies in it as assets of real time digital world daily generate massive volume of real-time data.In the job classification field accurate classification of jobs to line of work categories is important for matching job seekers with appropriate jobs. An example of such a job title classification system is an automatic text job post classification system that utilizes machine learning. Machine learning based job classification techniques for text and related entity have been well researched in academic world and have also been successfully applied in many industrial settings. In this paper we present a new approach, machine learning-based semi-supervised job title classification system. Our method influences a varied collection of classification and techniques to deal with the challenges of designing a scalable classification system for a large nomenclature of job categories. It encompasses these techniques in cascade classification architecture. We first present the architecture of our system, which consists of a two-stage Capture with filtration and fine level classification algorithm. The paper concludes by presenting experimental results on real world live data.


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