Machine Learning, Distributed and Parallel Computing, and High-performance Computing are the themes for this year’s edition of Fifth Elephant.
The deadline for submitting a proposal is 15th June 2015
We are looking for talks and workshops from academics and practitioners who are in the business of making sense of data, big and small.
This track is about general, novel, fundamental, and advanced techniques for making sense of data and driving decisions from data. This could encompass applications of the following ML paradigms:
- Statistical Visualizations
- Unsupervised Learning
- Supervised Learning
- Semi-Supervised Learning
- Active Learning
- Reinforcement Learning
- Monte-carlo techniques and probabilistic programming
- Deep Learning
Across various data modalities including multi-variate, text, speech, time series, images, video, transactions, etc.
This track is about tools and processes for collecting, indexing, and processing vast amounts of data. The theme includes:
- Distributed and Parallel Computing
- Real Time Analytics and Stream Processing
- MapReduce and Graph Computing frameworks
- Kafka, Spark, Hadoop, MPI
- Stories of parallelizing sequential programs
- Cost/Security/Disaster Management of Data
HasGeek believes in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like it to be available under a permissive open source license. If your software is commercially licensed or available under a combination of commercial and restrictive open source licenses (such as the various forms of the GPL), please consider picking up a sponsorship. We recognize that there are valid reasons for commercial licensing, but ask that you support us in return for giving you an audience. Your session will be marked on the schedule as a sponsored session.
If you are interested in conducting a hands-on session on any of the topics falling under the themes of the two tracks described above, please submit a proposal under the workshops section. We also need you to tell us about your past experience in teaching and/or conducting workshops.
Deep Learning for Natural Language Processing
This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart
[Update: 14th July] We have decided to open source the code for this talk as a toolkit. https://github.com/flipkart-incubator/optimus Feel free to use it to train your own classifiers, and contribute!
Much of the Text Mining needed in real-life boils down to Text Classification: be it prioritising e-mails received by Customer Care, categorising Tweets aired towards an Organisation, measuring impact of Promotions in Social Media, and (Aspect based) Sentiment Analysis of Reviews. These techniques can not only help gauge the customer’s feedback, but also can help in providing users a better experience.
Traditional solutions focused on heavy domain-specific Feature Engineering, and thats exactly where Deep Learning sounds promising!
We will depict our foray into Deep Learning with these classes of Applications in mind. Specifically, we will describe how we tamed Deep Convolutional Neural Network, most commonly applied to Computer Vision, to help classify (short) texts, attaining near-state-of-the-art results on several SemEval tasks consistently, and a few tasks of importance to Flipkart.
In this talk, we plan to cover the following:
Basics of Deep Learning as applied to NLP: Word Embeddings and its compositions a la Recursive Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.
New Experimental results on an array of SemEval / Flipkart’s internal tasks: e.g. Tweet Classification and Sentiment Analysis. (As an example we achieved 95% accuracy in binary sentiment classification task on our datasets - up from 85% by statistical models)
Share some of the learnings we have had while deploying these in Flipkart!
Here is a mindmap explaining the flow of content and key takeawys for the audience: https://atlas.mindmup.com/2015/06/4cbcef50fa6901327cdf06dfaff79cf0/deep_learning_for_natural_language_proce/index.html
Devashish is a software developer at Flipkart. His primary work is in the area of deriving insights from unstructured data. His previous works include Review Summarization at Flipkart, User Insights Platform - a system which enables us to dig into 100s of TB of data to find user insights. He is currently working on sentiment analysis and aspect extraction from Social Media.
Prerana Singhal is currently pursuing Masters of Technology in the department of Computer Science from IIT Bombay. She is doing her research work in the field Natural Language Processing under Professor Pushpak Bhattacharya, the principle area of interest being Sentiment Analysis and Deep Learning.
- Previous talk by speaker - “Know your customer - Deriving User Insights”: https://www.youtube.com/watch?v=sLDXm_53-mM
- Mind map for this talk: https://atlas.mindmup.com/2015/06/4cbcef50fa6901327cdf06dfaff79cf0/deep_learning_for_natural_language_proce/index.html