Best NLP competitions on Kaggle (to learn from) | Summary and Q&A

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December 13, 2020
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Abhishek Thakur
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Best NLP competitions on Kaggle (to learn from)

TL;DR

Explore various Kaggle competitions related to Natural Language Processing (NLP) to learn and apply NLP techniques.

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Questions & Answers

Q: What is the predict closed questions on Stack Overflow competition about?

This competition involves predicting if a question on Stack Overflow will be closed based on its title, body text, and tags. By analyzing the text data, participants can determine if a question lacks information or is not constructive, leading to its closure.

Q: What is the evergreen classification challenge about?

In this competition, participants categorize web pages from StumbleUpon as "evergreen" or not. The dataset includes URL, boilerplate text, category information, and a label indicating if the content is considered evergreen. Participants can learn about cleaning HTML, extracting text, and building classification models in NLP.

Q: How does the crowdflower search results relevance competition work?

The competition focuses on predicting the relevance of search results from e-commerce websites. Participants categorize the relevance of search results based on given queries and results, with a relevance scale from one to four. The data includes product descriptions, titles, and images, allowing participants to employ various NLP techniques for relevance classification.

Q: What is the objective of the home depot product search relevance competition?

In this competition, participants assign relevance scores to search results from Home Depot. Given product descriptions, titles, attributes, and customer search terms, participants build models that predict the relevance of the search results to the corresponding queries, using one to three as relevance scores.

Summary & Key Takeaways

  • The video discusses several Kaggle competitions related to NLP, such as predicting closed questions on Stack Overflow, evergreen classification challenge, and search result relevance.

  • Each competition involves different types of text data, such as titles, bodies, and tags, and focuses on different NLP techniques, such as cleaning HTML, extracting features, and building predictive models.

  • The video highlights the importance of starting with simple methods and gradually diving deeper into more advanced approaches in NLP competitions.

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