Browsing Data Science Institute by Author "http://dx.doi.org/10.13039/501100001588"

Now showing items 1-4 of 4

    • Automatic taxonomy generation: a use-case in the legal domain 

      Robin, Cécile; O'Neill, James; Buitelaar, Paul (LTC'17, 8th Language & Technology Conference, 2017-11-17)
      A key challenge in the legal domain is the adaptation and representation of the legal knowledge expressed through texts, in order for legal practitioners and researchers to access this information more easily and faster ...

    • Back-translation approach for code-switching machine translation: A case study 

      Masoud, Maraim; Torregrosa, Daniel; Buitelaar, Paul; Arčan, Mihael (AICS2019, 2019-12-05)
      Recently, machine translation has demonstrated significant progress in terms of translation quality. However, most of the research has focused on translating with pure monolingual texts in the source and the target side ...

    • Leveraging rule-based machine translation knowledge for under-resourced neural machine translation models 

      Torregrosa, Daniel; Pasricha, Nivranshu; Chakravarth, Bharathi Raja; Masoud, Maraim; Alonso, Juan; Casas, Noe; Arcan, Mihael (NUI Galway, 2019-08-19)
      Rule-based machine translation is a machine translation paradigm where linguistic knowledge is encoded by an expert in the form of rules that translate from source to target language. While this approach grants total ...

    • Towards an integrative approach for making sense distinctions 

      McCrae, John P.; Fransen, Theodorus; Ahmadi, Sina; Buitelaar, Paul; Goswami, Koustava (Frontiers Media, 2022-02-07)
      Word senses are the fundamental unit of description in lexicography, yet it is rarely the case that different dictionaries reach any agreement on the number and definition of senses in a language. With the recent rise in ...