Applying Dynamic Embeddings in Natural Language Processing to Analyze Text over Time

Many data scientists are familiar with word embedding models such as word2vec, which capture semantic similarity of words in a large corpus. However, word embeddings are limited in their ability to interrogate a corpus alongside other context or over time. Moreover, word embedding models either need significant amounts of data, or tuning through transfer learning of a domain-specific vocabulary that is unique to most commercial applications.

In this talk, I will introduce exponential family embeddings. Developed by Rudolph and Blei, these methods extend the idea of word embeddings to other types of high-dimensional data. I will demonstrate how they can be used to conduct advanced topic modeling on datasets that are medium-sized, which are specialized enough to require significant modifications of a word2vec model and contain more general data types (including categorical, count, continuous). I will discuss how my team implemented a dynamic embedding model using Tensor Flow and our proprietary corpus of job descriptions. Using both categorical and natural language data associated with jobs, we charted the development of different skill sets over the last 3 years. I will specifically focus the description of results on how tech and data science skill sets have developed, grown and pollinated other types of jobs over time.

 
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Outline/Structure of the Case Study

Key Question: How have data science skills changed over time?

  • Key approaches to identify changes among corpora:
    • Manual Feature Extraction
    • Dynamic Topic Models
  • Brief introduction to the power of 'vanilla' word embeddings (i.e. GLoVE or word2vec) including:
    • Semantic similarities
    • Entity relationships
    • Bias reflection
  • Pros and cons of pretrained embeddings
  • Approaches to dynamic embeddings
    • Static stitched together
    • Dynamic model trained together
  • Key results from experiments where we implemented dynamic embedding analysis of job descriptions
    • Small corpus identified gains and losses
    • Large corpus identified role-dependent shifts in requirements
  • Conclusion

Learning Outcome

(1) Lessons learnt from implementing different word embedding methods (from pretrained to custom);

(2) The impact of corpus size on insights gleaned from dynamic embeddings models;

(3) How data science skills have varied across industries, functions and over time.

Target Audience

Intermediate level Data Scientists and Data Engineers with an interest in NLP

Prerequisites for Attendees

A basic understanding of word embeddings is helpful but not required.

schedule Submitted 4 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Venkatraman J
    By Venkatraman J  ~  2 months ago
    reply Reply

    Hi Maryam Jahanshahi,

    Thanks for submitting the proposal. Very interesting topic for discussion. In terms of learning outcome is there any solid takeaway you will be giving to audience like a library which learns dynamic embeddings using transfer learning?.

     

     

     

    • Maryam Jahanshahi
      By Maryam Jahanshahi  ~  2 months ago
      reply Reply

      Hi Venkatraman!

      Thanks so much for reviewing! There is currently no library that implements dynamic embeddings. In my talk, I do link to the repo where the original implementation is and we have basically used that implementation to get the results of this talk. 

      Secondly, you mention conducting transfer learning on dynamic embeddings. To my knowledge, that is not something that has been done on dynamic models (i.e. dynamic topic models or dynamic embeddings).What I had proposed to talk about is how we can extract useful information even with small amounts of data when we train dynamic embeddings from scratch. I hope that makes it clear!

      • Venkatraman J
        By Venkatraman J  ~  2 months ago
        reply Reply

        Thanks for the response. That makes it clear!!.


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    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

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    Aamir Nazir - DeepMind Alpha Fold 101

    Aamir Nazir
    Aamir Nazir
    Student
    -
    schedule 1 month ago
    Sold Out!
    45 Mins
    Talk
    Intermediate

    "Today we’re excited to share DeepMind’s first significant milestone in demonstrating how artificial intelligence research can drive and accelerate new scientific discoveries. With a strongly interdisciplinary approach to our work, DeepMind has brought together experts from the fields of structural biology, physics, and machine learning to apply cutting-edge techniques to predict the 3D structure of a protein based solely on its genetic sequence." source: https://deepmind.com/blog/alphafold/

    Over the past five decades, scientists have been able to determine shapes of proteins in labs using experimental techniques like cryo-electron microscopy, nuclear magnetic resonance or X-ray crystallography, but each method depends on a lot of trial and error, which can take years and cost tens of thousands of dollars per structure. This is why biologists are turning to AI methods as an alternative to this long and laborious process for difficult proteins.

    Recently released by Deepmind, Alpha fold, beat top pharmaceutical companies with 100K+ employees like Pfizer, Novartis, etc. at predicting protein structures in the CASP13 challenge. It outperformed all the other competitors and emerged first with a huge difference of correctly predicting 25 proteins correctly whereas the second place winner only predicted 9 of them correctly and that too with only 29K of the 129K present data about different proteins

    This research is the greatest breakthrough in this field which will be able to predict how proteins fold for the formation of different types of proteins for different functions. This is important because this could lead to a better understanding and possibly a cure for diseases like Alzheimer's, mad cow's disease etc. because these diseases are believed to be caused due to malfunction in the folding of the proteins in the body.

    The architecture for the network was simple, on a high level it constituted of residual convolutional neural network and gradient descent to optimize full protein features in the end.

    The audience from this talk will be able to learn about how to reproduce the architecture of the Alpha Fold and also some basics about how different proteins strands affect the body and function of the proteins. This talk will be mostly on the technical side of the Alpha Fold.