Anthill Inside 2017

Lots of incident data is available in large corporate. However it is Noisy and inaccurate. Classification directly using TFIDF vectorization and machine learning models gives low accuracy. Lots of effort is spent in hand curation of data. Objective is to automatically extract features using deep learning techniques to get a higher lever representation of the text in the incidents. Downstream tasks like classification and clustering will be performed on this space. Presentation will cover the deep learning network architecture of such a system and the results obtained.

Outline

 Problem statement

 Objective

 Tasks
 Overview of Relevant Reference Literature
 What are we trying to do?
 Prior work
 View of data
 Challenge
 Proposed approach
 Network architecture
 Results
 Future work
 Conclusions

• What is Representation Learning? 1 min
• Representation Learning in NLP and it’s practical applications? 2 mins
• Introduction to specific problem we are trying to solve – 1 min
• Deep Learning Architecture - 4 mins
o Our NLP pre-processing pipeline
o Deep Learning network architecture we have used and Parameters trained with
• Our Results , Conclusions and Further work – Rest of time

• OUTCOMES/CONCLUSION
The participants will be exposed to the concept of Representation learning. They will understand the application of Representation learning to the feature extraction space. They will understand how to apply deep learning techniques for natural language processing. Specifically they will be exposed to the deep learning network architecture used, applicability of deep learning in the field of natural language processing, the algorithm used, how the weights are learnt, lessons learnt and results obtained. The unique contribution of this talk is the methodology of application of deep learning for automatically extracting features from the textual incident management domain data.

Requirements

None

Speaker bio

Arthi Venkataraman has 20+ years of experience in the design, development and testing of projects in different domains. She is currently a Senior Member in the Distinguished Members of Technical Staff cadre at Wipro Technologies. Her current role involves development of solutions which involve application of deep learning techniques to natural language processing with intent to better perform in different tasks like classification, clustering, question answering, summarization, etc.
Previously she has been involved in Bot development for different business problems spanning the area of Natural Language Processing, Machine Learning and Semantics Technologies She has a B.E Degree in Computer Science from University Visvesvariah College of Engineering, Bangalore and an MBA (PGDSM) from IIM, Bangalore.
She has previously presented papers and spoken at other international conferences with maximum audience sizes of many hundreds. This presentation is based on Arthi’s experience in feature extraction from natural language using deep learning techniques.

Links

• The https://github.com/arthiv1/MachineLearningInsights holds all the previously published papers of Arthi.
• She has published papers in ACM, IEEE as well as other journals. 1. International conferences spoken and presented papers at :
• • 9th International Conference on Machine Learning and Computing Proceedings at Singapore, February, 2017
• • International Conference on Theoretical Computing and Communication Technology, 2016
• • Grace Hopper 2015 - https://www.youtube.com/channel/UCGuAMvPJ0l9xscCfoxUpEDA
• • Pycon 2015 https://in.pycon.org/2015/
• • Artificial Intelligence IEEE conference Taiwan, 2015
• • Fifth Elephant 2013 https://fifthelephant.talkfunnel.com/2013/644-similar-entity-detection-in-large-data
• • Second International Conference on Data Mining, Internet Computing, and Big Data, Reduit, Mauritius 2015