Hybrid Enhanced U niversal D ependencies Parsing (2020)

Abstract: This paper describes our system to predict enhanced dependencies for Universal Dependencies (UD) treebanks, which ranked 2nd in the Shared Task on Enhanced Dependency Parsing with an average ELAS of 82.60%. Our system uses a hybrid two-step approach. First, we use a graph-based parser to extract a basic syntactic dependency tree. Then, we use a set of linguistic rules which generate the enhanced dependencies for the syntactic tree. The application of these rules is optimized using a classifier which predicts their suitability in the given context. A key advantage of this approach is its language independence, as rules rely solely on dependency trees and UPOS tags which are shared across all languages.

## introduction parsing enhanced universal dependencies (eud) @xcite is an interesting extension of dependency parsing. euds provide syntactic information which can be crucial for any nlp processing based on syntactic analysis. the shared task on eud parsing @xcite provided the platform to develop and compare various systems. our team participated using a hybrid system (machine learning/rule-based) which came second in both metrics, elas (82,6%) and eulas (84,6%). ## system description the data of universal dependencies treebanks @xcite foot_0 used for the shared task and annotated with enhanced dependencies (other than copied basic dependencies) is small. in total, all training treebanks contain about 5.1 million words, only 5.6% of those have a second enhanced dependency attached to them (the first being the copied basic dependency). another 7.2% and 8.3% of words have an enhanced dependency like obl:... or nmod:... which correspond to the basic dependency but also give the adposition and morphological case (if existing in the language in question). in total, only 21.1% or 1 million words have any non basic enhanced dependency. the enhanced dependencies address specific and well known linguistic phenomena, and are relatively deterministic @xcite , once the basic dependency tree is available. for this reason, we decided to utilise a hybrid system, using a graph-based parser to produce first a dependency tree and a rule system which uses the generated dependency tree to determin the enhanced dependencies. the latter uses a (learned) filter to control the application of rules in certain contexts. the system functions as a pipeline (cf. figure 1 ). thus errors in earlier parts of the pipeline will impact the results of the following components. ## conclusion and perspectives considering that training data was heterogeneous, partially incomplete, and in general not very voluminuous, our hybrid machine-learning (ml)/rulebased approach gave very good results for the shared task. a possible extension would be the processing of elided nodes. even if for the long term a purely ml-based approach may prove more efficient, at least our language-independent system can help to preannotate existing ud treebanks which, after human validation, can be the basis of an ml approach on predicting enhanced dependencies.