Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a
sentence. It is typically regarded as an important step in the standard NLP pipeline. As the …

In addition to a high accuracy, short parsing and training times are the most important
properties of a parser. However, parsing and training times are still relatively long. To …

For the 11th straight year, the Conference on Computational Natural Language Learning
has been accompanied by a shared task whose purpose is to promote natural language …

Most current dependency parsers presuppose that input words have been morphologically
disambiguated using a part-of-speech tagger before parsing begins. We present a …

We introduce a simple and accurate neural model for dependency-based semantic role
labeling. Our model predicts predicate-argument dependencies relying on states of a …

Meaning Representation (AMR) annotation efforts have mostly focused on English. In order
to train parsers on other languages, we propose a method based on annotation projection …

Joint morphological and syntactic analysis has been proposed as a way of improving
parsing accuracy for richly inflected languages. Starting from a transition-based model for …

We present a transition-based parser that jointly produces syntactic and semantic
dependencies. It learns a representation of the entire algorithm state, using stack long short …

We present LemmaTag, a featureless neural network architecture that jointly generates part-
of-speech tags and lemmas for sentences by using bidirectional RNNs with character-level …

This paper suggests two ways of improving transition-based, non-projective dependency
parsing. First, we add a transition to an existing non-projective parsing algorithm, so it can …