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). UAS does not consider the semantic relation (e.g. Subj) used to label the attachment between the head and the child, while LAS requires a semantic correct label for each attachment.Here, we also mention the predicted POS tagging accuracy.
The following results are just for references:
|Model||UAS||LAS||Note||Paper / Source|
|Stack-only RNNG (Kuncoro et al., 2017)||95.8||94.6||Constituent parser||What Do Recurrent Neural Network Grammars Learn About Syntax?|
|Deep Biaffine (Dozat and Manning, 2017)||95.75||94.22||Stanford conversion v3.5.0||Deep Biaffine Attention for Neural Dependency Parsing|
|Semi-supervised LSTM-LM (Choe and Charniak, 2016) (Constituent parser)||95.9||94.1||Constituent parser||Parsing as Language Modeling|
Cross-lingual zero-shot dependency parsing
Cross-lingual zero-shot parsing is the task of inferring the dependency parse of sentences from one language without any labeled training trees for that language.
Universal Dependency Treebank
Models are evaluated against the Universal Dependency Treebank v2.0. For each of the 6 target languages, models can use the trees of all other languages and English and are evaluated by the UAS and LAS on the target. The final score is the average score across the 6 target languages. The most common evaluation setup is to use gold POS-tags.
|Model||UAS||LAS||Paper / Source||Code|
|Cross-Lingual ELMo (Schuster et al., 2019)||84.2||77.3||Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing||Official|
|MALOPA (Ammar et al., 2016)||70.5||Many Languages, One Parser||Official|
|Guo et al. (2016)||76.7||69.9||A representation learning framework for multi-source transfer parsing|
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|
|Iterative reranking (Le & Zuidema, 2015)||66.2||Unsupervised Dependency Parsing - Let’s Use Supervised Parsers|
|Combined System (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 (Blunsom & Cohn, 2010)||55.7||Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing|
|Shared Logistic Normal DMV (Cohen & Smith, 2009)||41.4||Shared Logistic Normal Distributions for Soft Parameter Tying in Unsupervised Grammar Induction|
|DMV (Klein & Manning, 2004)||35.9||Corpus-Based Induction of Syntactic Structure - Models of Dependency and Constituency|