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 (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.
{% include table.html results=site.data.dependency_parsing.Penn_Treebank scores='POS,UAS,LAS' %}
The following results are just for references:
{% include table.html results=site.data.dependency_parsing.Reference scores='POS,UAS,comment' %}
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').
{% include table.html results=site.data.dependency_parsing.Unsupervised_Penn_Treebank scores='UAS' %}