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 (v3.3.0) 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). Here, we also mention the predicted POS tagging accuracy.
The following results are just for references:
|Model||POS||UAS||comment||Paper / Source||Code|
|Stack-only RNNG by Kuncoro et al. (2017)||95.8||Constituent parser||What Do Recurrent Neural Network Grammars Learn About Syntax?|
|Semi-supervised LSTM-LM by Choe and Charniak (2016)||95.9||Constituent parser||Parsing as Language Modeling|
|Deep Biaffine by Dozat and Manning (2017)||97.3||95.66||Stanford conversion **v3.5.0**||Deep Biaffine Attention for Neural Dependency Parsing||Official|
Unsupervised dependency parsing
Unsupervised dependency parsing is the task of inferring the dependency parse of sentences without any labeled training data.
As with supervised parsing, models are evaluated against the Penn Treebank. The most common evaluation setup is to use gold POS-tags as input and to evaluate systems using the unlabeled attachment score (also called ‘directed dependency accuracy’).
|Model||UAS||Paper / Source||Code|
|Iterative reranking by Le & Zuidema (2015)||66.2||Unsupervised Dependency Parsing - Let’s Use Supervised Parsers|
|Combined System by Spitkovsky et al (2013)||64.4||Breaking Out of Local Optima with Count Transforms and Model Recombination - A Study in Grammar Induction|
|Tree Substitution Grammar DMV by Blunsom & Cohn (2010)||55.7||Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing|
|Shared Logistic Normal DMV by Cohen & Smith (2009)||41.4||Shared Logistic Normal Distributions for Soft Parameter Tying in Unsupervised Grammar Induction|
|DMV by Klein & Manning (2004)||35.9||Corpus-Based Induction of Syntactic Structure - Models of Dependency and Constituency|