arrow_back How organizations can leverage 'Large Scale Graph Based Analytics’ to derive value from their data.
Deep Learning - An example implementation
Submitted by Krishna Bhavsar (@krishnabhavsar) on Tuesday, 17 April 2018
Section: Full talk Technical level: Beginner
In this talk I intend to showcase one of the problems I solved using Deep Learning framework recently. Resume Classification for a recruitment consultant agency. I shall go through the multiple approaches in which I tried to solve that problem, the obstacles I faced and finally how I came to the final solution.
AI vs ML vs Deep Learning
ANN vs Neurons in human brain
Example problems that can be solved using Deep Learning
Problem Definition for resume classificaiton
Formulation and generalization of a typical Candidate Resume
Intial Data modelling for the solution
- Number of samples - Entity Recognition algortihms - Preprocessing - Working at offset level - Feature encoding 7th Slide
Journey to the final solution
1. Start with a smaller problem - Try identifying only one of the Major entities by way of gaps
2. Expand the input and the problem and change encoding – Identify gaps present in between all the major entities in the document
3. Differentiate the problem and change the encoding back to 98 distinct features
4. 2nd pass on output of the 3rd stage to augment the result of the previous step.
Final solution and final data model
GATE + NN Multinomial(in two passes)
Insights in to the results
1st Pass identifies smaller sized entities more accurately
2nd Pass identifies larger sized entities more accurately
1st Pass introduces lots of explicit ouliers, which are very much not related to general location and size of entities
2nd Pass muddles with the smaller sized entity’s recogniton
Tools and technologies used
Krishna Bhavsar has spent around 10 years working on natural language processing, social media analytics, and text mining in various industry domains such as hospitality, banking, healthcare, and more. He has worked on analysing social media responses for popular television shows and popular retail brands and products. His first commercial Tech publication was released worldwide in November 2017, titled, Natural Language Processing with Python - Cookbook with Packt publishing house, UK. He has also published a paper on sentiment analysis augmentation techniques in 2010 NAACL. He recently created an NLP pipeline/toolset and open sourced it for public use. Krishna completed his Post Graduate Diploma in Data Sciences and Business Administration from Great Lakes Institute, Chennai in December 2017.
Apart from academics and technology, Krishna has a passion for motorcycles and football. In his free time, he likes to travel and explore. He has gone on pan-India road trips on his motorcycle and backpacking trips across most of the countries in South East Asia and Europe.
LinkedIn profile - https://www.linkedin.com/in/krishna-bhavsar1