Semi-Supervised Insight generation from petabyte scale Text data

Existing state-of-the-art supervised methods in Machine Learning require large amounts of annotated data to achieve good performance and generalization. However, manually constructing such a training data set with sentiment labels is a labor-intensive and time-consuming task. With the proliferation of data acquisition in domains such as images, text and video, the rate at which we acquire data is greater than the rate at which we can label them. Techniques that reduce the amount of labelled data needed to achieve competitive accuracies are of paramount importance for deploying scalable, data-driven, real-world solutions. Semi-Supervised Learning algorithms generally provide a way of learning about the structure of the data from the unlabelled examples, alleviating the need for labels.

At Envestnet | Yodlee, we have deployed several advanced state-of-the-art Machine Learning solutions which process millions of data points on a daily basis with very stringent service level commitments. A key aspect of our Natural Language Processing solutions is Semi-supervised learning (SSL): A family of methods that also make use of unlabelled data for training – typically a small amount of labelled data with a large amount of unlabelled data. Pure supervised solutions fail to exploit the rich syntactic structure of the unlabelled data to improve decision boundaries.

There is an abundance published work in the field - but few papers have succeeded in showing significantly better results than state-of-the-art supervised learning. Often, methods have simplifying assumptions that fail to transfer to real-world scenarios. There is a lack of practical guidelines for deploying effective SSL solutions. We attempt to bridge that gap by sharing our learning from successful SSL models deployed in production.

We will talk about best practices and challenges in deploying SSL solutions in NLP - We shall cover:

  1. Our findings while working on SSL.
  2. Techniques which have worked for us, and which have not
  3. Which SSL method is suitable to solve a given use-case.
  4. How to deal with different distributions for labelled and unlabelled data
  5. How to quantify the effectiveness of each point in our training data
  6. How to build a feedback loop that chooses points for training that result in the greatest accuracy boosts and
  7. The effect of relative sizes of labelled and unlabelled data







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Outline/Structure of the Case Study

  1. [5 minutes] Introduction to Semi-Supervised Learning and how does SSL helps to improve the performance of pure supervised ML model.
  2. [15 minutes] Primer on different SSL methods and how to use those to solve business problems.
  3. [10 minutes] How Data Science Team @Yodlee has leveraged SSL for mining insights from petabytes of data.
  4. [10 minutes] Systematic approach to build an SSL solution for industrial applications.
  5. [5 minutes] Conclusion and Q&A

Learning Outcome

In this session, attendees will learn about...

1. how Semi-Supervised learning helps us to leverage huge corpus of unlabelled data to improve pure Supervised ML model.

2. the systematic approach towards building a Semi-Supervised ML model.

3. what are the best use-cases to leverage Semi-Supervised Learning.

4. state-of-the-art methods of Semi-Supervised Learning.

Target Audience

This session will be highly relevant for Researcher and ML enthusiast who works on Data Science.

Prerequisites for Attendees

Basic understanding of Machine Learning and Deep Learning.

schedule Submitted 1 month ago

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