Generating Trees with Dynamic Programs (2/25/2020)
Content:
- What is Graph-based Parsing?
- Minimum Spanning Tree Parsing
- Structured Training and Other Improvements
- Dynamic Programming Methods for Phrase Structure Parsing
- Required Reading (for quiz): Constituency Parsing Jurafsky and Martin Chapter 13
- Reference: Eisner Algorithm (Eisner 1996)
- Reference: Large-margin Training of Parsers (McDonald et al. 2005)
- Reference: Spanning-tree Algorithms (McDonald et al. 2005)
- Reference: Higher-order Dependency Parsing (Zhang and McDonald 2012)
- Reference: Graph-based Parsing w/ Neural Nets (Pei et al. 2015)
- Reference: BiLSTM Features for Graph-based Parsing (Kiperwasser and Goldberg 2016)
- Reference: Deep Bi-affine Attention (Dozat and Manning 2017)
- Reference: Probabilistic Parsing w/ Matrix Tree Theorem (Koo et al. 2007)
- Reference: Neural Probabilistic Parser (Ma and Hovy 2017)
- Reference: Neural CRF Parsing (Durrett and Klein 2015)
- Reference: Span-based Constituency Parsing (Stern et al. 2017)
- Reference: Inside-outside Recurrent Networks (Le and Zuidema 2014)
- Reference: Parsing as Language Modeling (Choe and Charniak 2016)
- Reference: Disentangling Reranking Effects (Fried et al. 2017)
- Reference: Constituency Parsing with a Self-Attentive Encoder (Kitaev and Klein 2018)
Slides: DP Parsing Slides
Sample Code: DP Parsing Code Examples