Explore deep learning techniques for information extraction in this 41-minute lecture from MIT's Introduction to Deep Learning course. Delve into the fundamentals of information extraction, document schemas, and end-to-end deep learning philosophy. Learn about context-free grammars and their application in parsing with deep learning. Discover 2-dimensional parsing techniques and methods for handling noise in the parsing process. Examine experimental results and participate in a Q&A session. Access additional resources, including slides, lab materials, and code repositories, to further enhance your understanding of Deep Conditional Probabilistic Context Free Grammars (CPCFG) for information extraction.
Deep CPCFG for Information Extraction
Alexander Amini and Massachusetts Institute of Technology via YouTube
Overview
Syllabus
​ - Introduction
- What is information extraction?
- Types of information headers, line items, etc
- Representing document schemas
- Philosophy of end-to-end deep learning
- Context free grammars CFG
- Parsing with deep learning
- Learning objective and training
- 2 dimensional parsing
- Handling noise in the parsing
- Experimental results
- Question and answering
Taught by
https://www.youtube.com/@AAmini/videos
Tags
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