IJCoL · Italian Journal
of Computational Linguistics

Vol. 3, n. 1 · June 2017

Special Issue: Natural Language and Learning Machines

Editors: Roberto Basili, Dan Roth

This is the fifth issue of the Italian Journal of Computational Linguistics (IJCoL), published by the Italian Association of Computational Linguistics (AILC).  This monographic issue sheds light on the methods of Machine Learning, developed in Artificial Intelligence, in their application to the automatic treatment of linguistic phenomena. Edited by two Guest Editors, leading researchers on the national and international scene, the issue deals with a central topic in the field of contemporary computational linguistics, namely the role of Machine Learning in the study of linguistic phenomena related to the sphere of Syntax and Semantics.

This volume collects various contributions regarding the application of quantitative methods, in particular algebraic and neural methods, to phenomena and processes of strong applicative interest. In particular this issue investigates the application of these processes to the morpho-syntactic analysis of Italian texts, the automatic acquisition of emotional lexicons from large scale corpora and, last but not least, the automation of Question Answering from texts, through the adoption of deep neural methods and architectures.

THE COMPLETE VOLUME

Available at aAccademia University Press (registration required)

THE INDIVIDUAL ARTICLES

Can be downloaded directly from this page

· TABLE OF CONTENTS ·

Editorial Note
Roberto Basili, Dan Roth

DOWNLOAD p. 7-10

Question Dependent Recurrent Entity Network for Question Answering
Andrea Madotto, Giuseppe Attardi

DOWNLOAD p. 11-22

Learning Affect with Distributional Semantic Models
Lucia C. Passaro, Alessandro Bondielli, Alessandro Lenci

DOWNLOAD p. 23-36

Bi-directional LSTM-CNNs-CRF for Italian Sequence Labeling and Multi-Task Learning
Pierpaolo Basile, Pierluigi Cassotti, Lucia Siciliani, Giovanni Semeraro

DOWNLOAD p. 37-50

Multitask Learning with Deep Neural Networks for Community Question Answering
Daniele Bonadiman, Antonio Uva, Alessandro Moschitti

DOWNLOAD p. 51-65