Webinar on Information retrieval from unstructured text at scale using advanced deep learning

Online 21-09-2017 09:00 PM to 21-09-2017 10:00 PM
  • 242


  • Knowledge and Learning


This webinar talks about converting unstructured e-commerce text into structured format by leveraging multi-task deep learning.

The existing problem: e-sellers upload text information in unstructured or semi-structured format. E-commerce engines use naive text search techniques as a result of which search engine performance suffers because of less number irrelevant results. Can we convert unstructured text into a structured (key:value) format with high precision and high recall?

Challenges: Presence of multiple products in text description (e.g., blue top goes well with black jeans, which is the primary product?), semantic dis-ambiguity (e.g., 'Blue' as brand vs color, top as filler vs fashion category), context spread across long sentences (e.g., this is stunning red color top, ....., this sleeveless piece is a gem, ....) scale: more than 4 m e-fashion listings crawled, parsed and converted into structured format.

Let us dive into the solutions with this webinar.


  • Motivate the problem by examples
  • Key challenges and requirements to solve the problem
  • Bidirectional LSTM based deep network
  • Case Study and deployment challenges

About Speaker:

Vijay Gabale obtained his PhD in Computer Science and Engineering from IIT Bombay (2012), and subsequently worked with IBM Research Labs as Research Scientist for 2.5 years. He co-founded, Huew, a content-driven shopping destination that is organizing rich multi-media content on the web, and connecting it to e-commerce websites. Vijay has over 10 years of experience in working with cutting-edge machine learning, deep learning and networking systems.

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