Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical structure and defines the relationships between "head" words and words, which modify those heads.
Example:
root
|
| +-------dobj---------+
| | |
nsubj | | +------det-----+ | +-----nmod------+
+--+ | | | | | | |
| | | | | +-nmod-+| | | +-case-+ |
+ | + | + + || + | + | |
I prefer the morning flight through Denver
Relations among the words are illustrated above the sentence with directed, labeled arcs from heads to dependents (+ indicates the dependent).
Models are evaluated on the Stanford Dependency conversion of the Penn Treebank with predicted POS-tags. Punctuation symbols are excluded from the evaluation. Evaluation metrics are unlabeled attachment score (UAS) and labeled attachment score (LAS).
| Model | UAS | LAS | Paper / Source |
|---|---|---|---|
| Stack-only RNNG (Kuncoro et al., 2017) | 95.8 | 94.6 | What Do Recurrent Neural Network Grammars Learn About Syntax? |
| Semi-supervised LSTM-LM (Choe and Charniak, 2016) | 95.9 | 94.1 | Parsing as Language Modeling |
| Deep Biaffine (Dozat and Manning, 2017) | 95.66 | 94.03 | Deep Biaffine Attention for Neural Dependency Parsing |
| Andor et al. (2016) | 94.61 | 92.79 | Globally Normalized Transition-Based Neural Networks |
| Distilled neural FOG (Kuncoro et al., 2016) | 94.26 | 92.06 | Distilling an Ensemble of Greedy Dependency Parsers into One MST Parser |
| Weiss et al. (2015) | 94.0 | 92.0 | Structured Training for Neural Network Transition-Based Parsing |
| Arc-hybrid (Ballesteros et al., 2016) | 93.56 | 91.42 | Training with Exploration Improves a Greedy Stack-LSTM Parser |
| BIST parser (Kiperwasser and Goldberg, 2016) | 93.2 | 91.2 | Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations |