A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing

We present a novel neural network model that learns POS tagging and graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to learn feature representations shared for both POS tagging and dependency parsing tasks, thus handling the feature-engineering problem. Our extensive experiments, on 19 languages from the Universal Dependencies project, show that our model outperforms the state-of-the-art neural network-based Stack-propagation model for joint POS tagging and transition-based dependency parsing, resulting in a new state of the art. Our code is open-source and available together with pre-trained models at: https://github.com/datquocnguyen/jPTDPComment: v2: also include universal POS tagging, UAS and LAS accuracies w.r.t gold-standard segmentation on Universal Dependencies 2.0 - CoNLL 2017 shared task test data; in CoNLL 201

National Sweatfree Summit 2010

This document is part of a digital collection provided by the Martin P. Catherwood Library, ILR School, Cornell University, pertaining to the effects of globalization on the workplace worldwide. Special emphasis is placed on labor rights, working conditions, labor market changes, and union organizing.ILRF_NationalSweatfreeSummit2010Notes.pdf: 96 downloads, before Oct. 1, 2020

D7.3 Training materials

This Deliverable gives a detailed description of the comprehensive training programme and of the open educational content that the University of Padua has accomplished up to now for the project "Linked Heritage: Coordination of standard and technologies for the enrichment of Europeana" (CIP Best Practice Network). The final version of D7.3 will be released by the end of the project, when all the Learning Objects will be finished