1 code implementation • LREC 2020 • Sina Ahmadi, John Philip McCrae, Sanni Nimb, Fahad Khan, Monica Monachini, Bolette Pedersen, Thierry Declerck, Tanja Wissik, Bell, Andrea i, Irene Pisani, Thomas Troelsg{\aa}rd, Sussi Olsen, Simon Krek, Veronika Lipp, Tam{\'a}s V{\'a}radi, L{\'a}szl{\'o} Simon, Andr{\'a}s Gyorffy, Carole Tiberius, Tanneke Schoonheim, Yifat Ben Moshe, Maya Rudich, Raya Abu Ahmad, Dorielle Lonke, Kira Kovalenko, Margit Langemets, Jelena Kallas, Oksana Dereza, Theodorus Fransen, David Cillessen, David Lindemann, Mikel Alonso, Ana Salgado, Jos{\'e} Luis Sancho, Rafael-J. Ure{\~n}a-Ruiz, Jordi Porta Zamorano, Kiril Simov, Petya Osenova, Zara Kancheva, Ivaylo Radev, Ranka Stankovi{\'c}, Andrej Perdih, Dejan Gabrovsek
Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography.
no code implementations • LREC 2020 • Fahad Khan, Laurent Romary, Ana Salgado, Jack Bowers, Mohamed Khemakhem, Toma Tasovac
In this article we will introduce two of the new parts of the new multi-part version of the Lexical Markup Framework (LMF) ISO standard, namely part 3 of the standard (ISO 24613-3), which deals with etymological and diachronic data, and Part 4 (ISO 24613-4), which consists of a TEI serialisation of all of the prior parts of the model.
no code implementations • LREC 2020 • Fahad Khan
The increasing recognition of the utility of Linked Data as a means of publishing lexical resource has helped to underline the need for RDF based data models which have the flexibility and expressivity to be able to represent the most salient kinds of information contained in such resources as structured data, including, notably, information relating to time and the temporal dimension.
no code implementations • 23 May 2019 • Laurent Romary, Mohamed Khemakhem, Fahad Khan, Jack Bowers, Nicoletta Calzolari, Monte George, Mandy Pet, Piotr Bański
Lexical Markup Framework (LMF) or ISO 24613 [1] is a de jure standard that provides a framework for modelling and encoding lexical information in retrodigitised print dictionaries and NLP lexical databases.
no code implementations • WS 2016 • Fahad Khan, Bell, Andrea i, Monica Monachini
This article describes work on enabling the addition of temporal information to senses of words in linguistic linked open data lexica based on the lemonDia model.
no code implementations • LREC 2016 • Ouafae Nahli, Francesca Frontini, Monica Monachini, Fahad Khan, Arsalan Zarghili, Mustapha Khalfi
This paper describes the conversion into LMF, a standard lexicographic digital format of {`}al-q{\=a}m{\=u}s al-muḥ{\=\i}ṭ, a Medieval Arabic lexicon.
no code implementations • LREC 2016 • Riccardo Del Gratta, Francesca Frontini, Monica Monachini, Gabriella Pardelli, Irene Russo, Roberto Bartolini, Fahad Khan, Claudia Soria, Nicoletta Calzolari
This proposal describes a new way to visualise resources in the LREMap, a community-built repository of language resource descriptions and uses.
no code implementations • LREC 2014 • Massimo Moneglia, Susan Brown, Francesca Frontini, Gloria Gagliardi, Fahad Khan, Monica Monachini, Aless Panunzi, ro
IMAGACT makes explicit the variation of meaning of action verbs within one language and allows comparisons of verb variations within and across languages.