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.
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|