The Fifth Elephant 2019

Gathering of 1000+ practitioners from the data ecosystem

Tickets

FlashText – A Python Library 28x faster than Regular Expressions for NLP tasks

Submitted by Nandan Thakur (@nthakur20) on Friday, 14 June 2019


Preview video

Session type: Short talk of 20 mins

Abstract

Data Science starts with data cleaning. When developers are working with text, they often clean it up first. Sometimes by replacing keywords (“Javascript” with “JavaScript”) while other times, to find out whether a keyword (“JavaScript”) was mentioned in a document. In today’s fast-moving world, bigger and bigger datasets are coming up with tens of thousands to millions of documents. the amount of time one would want to invest in cleaning these gigantic datasets would take them days using RegEx (5 days ~ 20K keywords and 3 Million documents). Therefore, FlashText - a super blazingly fast library reduced days of computation time into few minutes (15mins ~ 20K keywords and 3 Million documents). FlashText is efficient at both extracting keywords and replacing them in sentences and has been implemented using the Aho-Corasick algorithm and the Trie Data Structure approach.

Outline

[0-3mins]: Brief Introduction about Myself. Introduction to FlashText and compare FlashText vs. Regular Expressions Performance.

[3-8mins]: How is FlashText so blazingly fast?

[8-10mins]: When to Use FlashText?

[10-12mins]: Installing FlashText.

[12-15mins]: UseCase 1: Code – Searching for words in a text document

[15-18mins]: UseCase 2: Code – Replacing words in a text document

[18-20mins]: End Notes and Feedback for Future Talks.

Requirements

Not a workshop

Speaker bio

I am a perpetual, quick learner and keen to explore the realm of Data Analytics and Science. I am deeply excited about the times we live in and the rate at which data is being generated and being transformed as an asset. I am well versed in domains such as Natural Language Processing, Machine Learning, and Signal Processing and share a keen interest in learning interdisciplinary concepts involving Machine Learning.

Links

Slides

https://drive.google.com/open?id=1WZ6MU80Qoz5znd89p9aSzTKxAor4Mo6zMvF2qPKqRyA

Preview video

https://youtu.be/s8WP79QU1zw

Comments

  • Abhishek Balaji (@booleanbalaji) Reviewer 4 months ago

    Hi Nandan,

    Thank you for submitting a proposal. We need to see detailed slides and a preview video to evaluate your proposal. Your slides must cover the following:

    • Problem statement/context, which the audience can relate to and understand. The problem statement has to be a problem (based on this context) that can be generalized for all.
    • What were the tools/frameworks available in the market to solve this problem? How did you evaluate these, and what metrics did you use for the evaluation? Why did you pick the option that you did?
    • Explain how the situation was before the solution you picked/built and how it changed after implementing the solution you picked and built? Show before-after scenario comparisons & metrics.
    • What compromises/trade-offs did you have to make in this process?
    • What is the one takeaway that you want participants to go back with at the end of this talk? What is it that participants should learn/be cautious about when solving similar problems?

    We need your updated slides and preview video by Jun 27, 2019 to evaluate your proposal. If we do not receive an update, we’d be moving your proposal for evaluation under a future event.

  • Nandan Thakur (@nthakur20) Proposer 4 months ago

    Hi Abhishek,

    Thanks for all the feedback, it helped me prepare my slides.

    I have created and attached my slides with this proposal yesterday itself. Please review them once if possible and let me know where to improve, if required.

    Thanks,
    Nandan

    • Abhishek Balaji (@booleanbalaji) Reviewer 4 months ago

      Thanks Nadan, moving this for evaluation

      • Nandan Thakur (@nthakur20) Proposer 3 months ago

        Hey Abhishek, Any Updates? Do let me know.
        Sorry to bother you.

        Best,
        Nandan

      • Nandan Thakur (@nthakur20) Proposer 3 months ago

        Hi Abhishek, Since I noticed you have been replying actively for my GuidedLDA talk. I wanted to get a final decision over the flashtext talk. I have improved the slides as well, you can take a look and let me know. Thanks :)

  • Abhishek Balaji (@booleanbalaji) Reviewer 3 months ago

    Haha, you caught me! On this proposal, we’d love to have a deep dive into Flashtext, since this proposal also talks about a project: https://hasgeek.com/fifthelephant/2019/proposals/how-go-food-built-a-query-semantics-engine-to-help-9BcHbCPcWSjA6nPRre7CQY

    Do you want to take this up in the flash talk session? You can skip over the installation parts and make it very crisp.

    • Nandan Thakur (@nthakur20) Proposer 3 months ago

      Hi Abhishek, Again I cannot seem to compress the talk within almost a 5 min flash talk. I guess I would end up hurrying things and not convey them properly. I would take atleast 20 mins as suggested. Is there no possibility to include a 20 min talk for the same?

      • Abhishek Balaji (@booleanbalaji) Reviewer 3 months ago

        Is Flashtext your project? And if it is, are you looking for feedback from the community on this project? If so, you can present it at the Demo sessions happening for about 10-15 mins and seek feedback from the audience.

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