Parsito is a fast open-source dependency parser written in C++. Parsito is based on greedy transition-based parsing, it has very high accuracy and achieves a throughput of 30K words per second. Parsito can be trained on any input data without feature engineering, because it utilizes artificial neural network classifier. Trained models for all treebanks from Universal Dependencies project are available (37 treebanks as of Dec 2015). Parsito is a free software under Mozilla Public License 2.0 (http://www.mozilla.org/MPL/2.0/) and the linguistic models are free for non-commercial use and distributed under CC BY-NC-SA (http://creativecommons.org/licenses/by-nc-sa/4.0/) license, although for some models the original data used to create the mo...
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank a...
This paper studies the performance of different parsers over a large Spanish treebank. The aim of th...
To accomplish the shared task on dependency parsing we explore the use of a linear transition-based ...
Parsing models for all Universal Depenencies 1.2 Treebanks, created solely using UD 1.2 data (http:/...
In the last decade, many accurate dependency parsers have been made publicly available. It can be di...
Dependency parsing is an important task in NLP, and it is used in many downstream tasks for analyzin...
Accurate dependency parsing requires large treebanks, which are only available for a few languages. ...
In this paper we present a system for experimenting with combinations of dependency parsers. The sys...
Almost all current dependency parsers classify based on millions of sparse indi-cator features. Not ...
DeSR is a statistical transition-based dependency parser which learns from annotated corpora which a...
A dependency parser consists in inducing a model that is capable of extracting the right dependency...
We present a simple and effective semisupervised method for training dependency parsers. We focus on...
As the interest of the NLP community grows to develop several treebanks also for languages other tha...
A set of continuous feature vectors formed by right singular vectors of a transformed co-occurrence ...
We present a study that compares data-driven dependency parsers obtained by means of annotation proj...
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank a...
This paper studies the performance of different parsers over a large Spanish treebank. The aim of th...
To accomplish the shared task on dependency parsing we explore the use of a linear transition-based ...
Parsing models for all Universal Depenencies 1.2 Treebanks, created solely using UD 1.2 data (http:/...
In the last decade, many accurate dependency parsers have been made publicly available. It can be di...
Dependency parsing is an important task in NLP, and it is used in many downstream tasks for analyzin...
Accurate dependency parsing requires large treebanks, which are only available for a few languages. ...
In this paper we present a system for experimenting with combinations of dependency parsers. The sys...
Almost all current dependency parsers classify based on millions of sparse indi-cator features. Not ...
DeSR is a statistical transition-based dependency parser which learns from annotated corpora which a...
A dependency parser consists in inducing a model that is capable of extracting the right dependency...
We present a simple and effective semisupervised method for training dependency parsers. We focus on...
As the interest of the NLP community grows to develop several treebanks also for languages other tha...
A set of continuous feature vectors formed by right singular vectors of a transformed co-occurrence ...
We present a study that compares data-driven dependency parsers obtained by means of annotation proj...
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank a...
This paper studies the performance of different parsers over a large Spanish treebank. The aim of th...
To accomplish the shared task on dependency parsing we explore the use of a linear transition-based ...