![](https://ccweb.imgix.net/https%3A%2F%2Fwww.classcentral.com%2Fimages%2Ficon-black-friday.png?auto=format&ixlib=php-4.1.0&s=fe56b83c82babb2f8fce47a2aed2f85d)
Overview
![](https://ccweb.imgix.net/https%3A%2F%2Fwww.classcentral.com%2Fimages%2Ficon-black-friday.png?auto=format&ixlib=php-4.1.0&s=fe56b83c82babb2f8fce47a2aed2f85d)
Syllabus
Intro
Some Connections to Tasks over Documents
Document Level Language Modeling
What Context to Incorporate?
How to Evaluate Document Coherence Models?
Mention(Noun Phrase) Detection
Components of a Coreference Model . Like a traditional machine learning model
Coreference Models:Instances
Mention Pair Models
Entity Models
Advantages of Neural Network Models for Coreference
Coreference Resolution w/ Entity- Level Distributed Representations
End-to-End Neural Coreference (Span Model)
End-to-End Neural Coreference (Coreference Model)
Using Coreference in Neural Models
Document Problems: Discourse Parsing
Shift-reduce Parsing Discourse Structure Parsing w/ Distributed Representations (Ji and Eisenstein 2014) . Shift-reduce parser with features from 2 stack elements and queue element
Discourse Parsing w/ Attention- based Hierarchical Neural Networks
Uses of Discourse Structure in Neural Models
Taught by
Graham Neubig