no code implementations • LREC 2020 • Federico Sangati, Antonio Pascucci, Johanna Monti
In this paper, we describe a Telegram bot, Mago della Ghigliottina (Ghigliottina Wizard), able to solve La Ghigliottina game (The Guillotine), the final game of the Italian TV quiz show L{'}Eredit{\`a}.
no code implementations • LREC 2020 • Christos Rodosthenous, Verena Lyding, Federico Sangati, Alex K{\"o}nig, er, Umair ul Hassan, Lionel Nicolas, Jolita Horbacauskiene, Anisia Katinskaia, Lavinia Aparaschivei
In this work, we report on a crowdsourcing experiment conducted using the V-TREL vocabulary trainer which is accessed via a Telegram chatbot interface to gather knowledge on word relations suitable for expanding ConceptNet.
no code implementations • LREC 2020 • Lionel Nicolas, Verena Lyding, Claudia Borg, Corina Forascu, Kar{\"e}n Fort, Katerina Zdravkova, Iztok Kosem, Jaka {\v{C}}ibej, {\v{S}}pela Arhar Holdt, Alice Millour, Alex K{\"o}nig, er, Christos Rodosthenous, Federico Sangati, Umair ul Hassan, Anisia Katinskaia, Anabela Barreiro, Lavinia Aparaschivei, Yaakov HaCohen-Kerner
We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be involved.
no code implementations • LREC 2020 • Giulia Speranza, Maria Pia di Buono, Johanna Monti, Federico Sangati
Terminological resources have proven crucial in many applications ranging from Computer-Aided Translation tools to authoring softwares and multilingual and cross-lingual information retrieval systems.
no code implementations • RANLP 2019 • Verena Lyding, Christos Rodosthenous, Federico Sangati, Umair ul Hassan, Lionel Nicolas, Alex K{\"o}nig, er, Jolita Horbacauskiene, Anisia Katinskaia
In this paper, we present our work on developing a vocabulary trainer that uses exercises generated from language resources such as ConceptNet and crowdsources the responses of the learners to enrich the language resource.
1 code implementation • 30 Jan 2018 • Barbara McGillivray, Federico Sangati
The present report summarizes an exploratory study which we carried out in the context of the COST Action IS1310 "Reassembling the Republic of Letters, 1500-1800", and which is relevant to the activities of Working Group 3 "Texts and Topics" and Working Group 2 "People and Networks".
no code implementations • WS 2017 • Agata Savary, Carlos Ramisch, Silvio Cordeiro, Federico Sangati, Veronika Vincze, Behrang Qasemizadeh, C, Marie ito, Fabienne Cap, Voula Giouli, Ivelina Stoyanova, Antoine Doucet
This paper presents the corpus annotation methodology and outcome, the shared task organisation and the results of the participating systems.
no code implementations • LREC 2016 • Angelo Basile, Federico Sangati
In this paper we describe 1) the process of converting a corpus of Dante Alighieri from a TEI XML format in to a pseudo-CoNLL format; 2) how a pos-tagger trained on modern Italian performs on Dante{'}s Italian 3) the performances of two different pos-taggers trained on the given corpus.
no code implementations • LREC 2016 • Gyri Sm{\o}rdal Losnegaard, Federico Sangati, Carla Parra Escart{\'\i}n, Agata Savary, Sascha Bargmann, Johanna Monti
We also discuss the problems we have detected upon examination of the data as well as possible ways of enhancing the survey.
no code implementations • TACL 2013 • Federico Sangati, Frank Keller
In this paper, we present the first incremental parser for Tree Substitution Grammar (TSG).