Endow the gift of eloquence to your NLP applications using pre-trained word embeddings

Word embeddings are the plinth stones of Natural Language Processing (NLP) applications, used to transform human language into vectors that can be understood and processed by machine learning algorithms. Pre-trained word embeddings enable transfer of prior knowledge about the human language into a new application thereby enabling rapid creation of a scalable and efficient NLP applications. Since the emergence of word2vec in 2013, the word embeddings field has seen rapid developments by leaps and bounds with each new successive word embedding outperforming the prior one.

The goal of this talk is to demonstrate the efficacy of using pre-trained word embedding to create scalable and robust NLP applications, and to explain to the audience the underlying theory of word embeddings that makes it possible. The talk will cover prominent word vector embeddings such as BERT and ELMo from the recent literature.

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Outline/Structure of the Talk

1. What are word embeddings? (5 minutes)
2. Creating custom word embeddings using dimensionality reduction (10 minutes)
3. Introduction to ELMo and BERT (15 minutes)
4. Use of pre-trained word embedding for text classification tasks using BERT (10 minutes)
5. Conclusion and Questions (5 minutes)

Learning Outcome

Knowledge of the history and the theory of word embeddings. Using state of the art pre-trained word embeddings for creating scalable natural language processing applications

Target Audience

Beginner/Mid-level experience with Natural Language Processing (NLP)

schedule Submitted 1 month ago

Public Feedback

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  • Ashay Tamhane
    By Ashay Tamhane  ~  2 weeks ago
    reply Reply

    Thanks Dr. Atul for the proposal. Could you kindly clarify if you would be covering any industry use cases in your talk?

    • Dr. Atul Singh
      By Dr. Atul Singh  ~  2 weeks ago
      reply Reply

      For the demo in part 4 of the talk, I intend to use the kaggle dataset on https://www.kaggle.com/c/two-sigma-financial-news for "Two Sigma: Using News to Predict Stock Movements" with stock price movement mapped to categories.

      I will not be covering an industry use case but will be using my technical experience from solving the industry use cases.

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    [1] https://www.cse.iitb.ac.in/~shivaram/papers/ks_adprl_2011.pdf

    [2] https://ai.google/research/pubs/pub44806

    [3] https://openai.com/five/

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    [5] http://cs231n.stanford.edu/reports/2017/pdfs/614.pdf

    [6] https://arxiv.org/pdf/1709.07174.pdf

    [7] https://en.wikipedia.org/wiki/Motion_capture

    [8] https://arxiv.org/pdf/1704.06888v3.pdf

    [9] https://bair.berkeley.edu/blog/2018/06/28/daml/

    [10] https://arxiv.org/pdf/1805.11592v2.pdf

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    This workshop will focus on building such a knowledge graph from unstructured text.

    Learn good research practices like organizing code and modularizing output for productive data wrangling to improve algorithm performance.

    Knowledge Graph at Embibe

    We will showcase how Embibe's proprietary Knowledge Graph manifests and how it's leveraged across a multitude of projects in our Data Science Lab.

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    Now, the present scenario encourages designing, developing, testing of medicine based on existing genetic insights and models. Deep learning models are helping to analyze and interpreting tiny genetic variations ( like SNPs – Single Nucleotide Polymorphisms) which result in unraveling of crucial cellular process like metabolism, DNA wear and tear. These models are also responsible in identifying disease like cancer risk signatures from various body fluids. They have the immense potential to revolutionize healthcare ecosystem. Clinical data collection is not streamlined and done in a haphazard manner and the requirement of data to be amenable to a uniform fetchable and possibility to be combined with genetic information would power the value, interpretation and decisive patient treatment modalities and their outcomes.

    There is hugh inflow of medical data from emerging human wearable technologies, along with other health data integrated with ability to do quickly carry out complex analyses on rich genomic databases over the cloud technologies … would revitalize disease fighting capability of humans. Last but still upcoming area of application in direct to consumer genomics (success of 23andMe).

    This road map promises an end-to-end system to face disease in its all forms and nature. Medical research, and its applications like gene therapies, gene editing technologies like CRISPR, molecular diagnostics and precision medicine could be revolutionized by tailoring a high-throughput computing method and its application to enhanced genomic datasets.

  • Liked Ramanathan R

    Ramanathan R / Gurram Poorna Prudhvi - Time Series analysis in Python

    480 Mins

    “Time is precious so is Time Series Analysis”

    Time series analysis has been around for centuries helping us to solve from astronomical problems to business problems and advanced scientific research around us now. Time stores precious information, which most machine learning algorithms don’t deal with. But time series analysis, which is a mix of machine learning and statistics helps us to get useful insights. Time series can be applied to various fields like economy forecasting, budgetary analysis, sales forecasting, census analysis and much more. In this workshop, We will look at how to dive deep into time series data and make use of deep learning to make accurate predictions.

    Structure of the workshop goes like this

    • Basics of time series analysis
    • Understanding Time series data with pandas
    • Preprocessing Time Series data
    • Classical Time series models (AR, MA, ARMA, ARIMA, SARIMA, GARCH, E-GARCH)
    • Forecasting with MLP (Multi-Layer Perceptron)
    • Forecasting with RNN (Recurrent Neural Network)
    • Forecasting with LSTM (Long Short Term Memory Network)
    • Understanding Financial Time Series data and forecasting with RNN and LSTM
    • Boosting techniques in Time series data
    • Developing intuition to choose the right network.
    • Dealing with large scale Time Series data

    Libraries Used:

    • Keras (with Tensorflow backend)
    • matplotlib
    • pandas
    • statsmodels
    • prophet
    • pyflux
    • tsfresh
  • Liked Amit  Baldwa


    45 Mins

    Machine learning provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

    Technical analysis shows in graphic form investor sentiment, both greed and fear. Technical analysis attempts to use past stock price and volume information to predict future price movements. Technical analysis of various indicators has been a time-tested strategy for seasoned traders and hedge funds, who have used these techniques to effective turn our profits in Securities Industry.

    Some researchers claim that stock prices conform to the theory of random walk, which is that the future path of the price of a stock is not more predictable than random numbers. However, Stock prices do not follow random walks.

    We will evaluate whether stock returns can be predicted based on historical information.

    Coupled with Machine Learning, we further try to decipher the correlation between the various indicators and identify the set of indicators which appropriately predict the value

  • Liked Samiran Roy

    Samiran Roy / Shibsankar Das - Semi-Supervised Insight generation from petabyte scale Text data

    45 Mins
    Case Study

    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


    [1] https://arxiv.org/pdf/1804.09170.pdf

    [2] http://www.acad.bg/ebook/ml/MITPress-%20SemiSupervised%20Learning.pdf

    [3] https://github.com/brain-research/realistic-ssl-evaluation

    [4] https://arxiv.org/pdf/1511.01432.pdf

    [5] http://pages.cs.wisc.edu/~jerryzhu/pub/sslicml07.pdf

  • Liked Vishnu Murali

    Vishnu Murali - Deep learning for predictive maintenance : Towards Industry 4.0

    45 Mins

    Why Industry 4.0 matters?

    Just 13 % of organizations have attained the complete effect in their digital investments, so empowering them is in demand to have financial upside and make digital expansion. The optimal combination of analytics/deep learning with IoT can save large and SME’s around $16 billion.

    What’s predictive maintenance (PdM) of Industrial physical assets?

    This is a online-monitoring system which requires hardware and software components, including condition monitoring sensors, gateways and modules to handle data processing and transmission, and a secured cloud server to handle data storage and data analytics.

    Why is this important to Industries?

    Cost, safety, availability, and reliability are the main reasons why key industrial players are investing in predictive maintenance. Predictive maintenance allows factories to monitor the condition of in-service equipment by measuring key parameters like vibration, temperature, pressure, and current. Such monitoring requires connected smart sensors featuring a high-speed signal chain, powerful processing, and wired and/or wireless connectivity.


    Considering the above sections, as in the case of any machine learning implementations, there are hidden and underlying challenges involved in implementing PdM for industries.

    To tackle this, our research group has come up with focused solution to seamlessly integrate machine learning algorithms and industrial IoT platform. The real challenge is twofold. Apart from the technical trials, this is more of a need for agreement among plant engineers and research community.

    Ambitious foresight

    • To bring awareness among engineers about industry 4.0
    • To have technically sound way of implementing PdM
    • Providing deliverables and have ROI

    Keywords: Predictive maintenance, Industry 4.0, Behavioral change