Papers by Francesca Frontini
Language Technologies for the Creation of Multilingual Terminologies. Lessons Learned from the SSHOC Project (2022.lrec-1)
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| Challenge: | Language Technologies can help in promoting and facilitating multilingualism in the Social Sciences and Humanities domain. |
| Approach: | They propose to use Natural Language Processing and Machine Translation to provide tools to foster multilingual access and discovery to SSH content across different languages. |
| Outcome: | The proposed tools prove to be a valid asset to translation tasks . validation of results by domain experts proficient in the language is an unavoidable phase of the whole workflow. |
One Language to rule them all: modelling Morphological Patterns in a Large Scale Italian Lexicon with SWRL (L18-1)
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| Challenge: | Linked data (LD) is a popular way of publishing lexical resources, but technical limitations and potentialities of LD are not understood as they should be. |
| Approach: | They propose to use the Semantic Web Rule Language to encode morphological patterns for a lexicographic publication as linked open data. |
| Outcome: | The proposed language allows the automatic derivation of inflectional variants of entries in the lexicon. |
MultiLexBATS: Multilingual Dataset of Lexical Semantic Relations (2024.lrec-main)
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Dagmar Gromann, Hugo Goncalo Oliveira, Lucia Pitarch, Elena-Simona Apostol, Jordi Bernad, Eliot Bytyçi, Chiara Cantone, Sara Carvalho, Francesca Frontini, Radovan Garabik, Jorge Gracia, Letizia Granata, Fahad Khan, Timotej Knez, Penny Labropoulou, Chaya Liebeskind, Maria Pia Di Buono, Ana Ostroški Anić, Sigita Rackevičienė, Ricardo Rodrigues, Gilles Sérasset, Linas Selmistraitis, Mahammadou Sidibé, Purificação Silvano, Blerina Spahiu, Enriketa Sogutlu, Ranka Stanković, Ciprian-Octavian Truică, Giedre Valunaite Oleskeviciene, Slavko Zitnik, Katerina Zdravkova
| Challenge: | Prior work has focused on analysing lexical semantic relations in word embeddings or probing pretrained language models (PLMs) with some exceptions. |
| Approach: | They propose to use a multilingual parallel dataset of lexical semantic relations adapted from BATS in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian as an experiment on cross-lingual transfer of relational knowledge. |
| Outcome: | The proposed dataset is adapted from a BATS-based dataset in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian. |