Browsing Data Science Institute by Author "Robin, Cécile"

Now showing items 1-8 of 8

    • 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 ...

    • Classifying sentential modality in legal language: A use case in financial regulations, acts and directives 

      O'Neill, James; Buitelaar, Paul; Robin, Cécile; O'Brien, Leona (ACM, 2017-06-12)
      Texts expressed in legal language are often di cult and time consuming for lawyers to read through, particularly for the purpose of identifying relevant deontic modalities (obligations, prohibitions and permissions). ...

    • A decade of Semantic Web research through the lenses of a mixed methods approach 

      Kirrane, Sabrina; Sabou, Marta; Fernandez, Javier D.; Osborne, Francesco; Robin, Cécile; Buitelaar, Paul; Motta, Enrico; Polleres, Axel (IOS Press, 2019-06-20)
      The identification of research topics and trends is an important scientometric activity, as it can help guide the direction of future research. In the Semantic Web area, initially topic and trend detection was primarily ...

    • Intent classification by the use of automatically generated knowledge graphs 

      Arcan, Mihael; Manjunath, Sampritha; Robin, Cécile; Verma, Ghanshyam; Pillai, Devishree; Sarkar, Simon; Dutta, Sourav; Assem, Haytham; McCrae, John P.; Buitelaar, Paul (MDPI, 2023-05-12)
      Intent classification is an essential task for goal-oriented dialogue systems for automatically identifying customers¿ goals. Although intent classification performs well in general settings, domain-specific user goals can ...

    • MixedEmotions: An open-source toolbox for multi-modal emotion analysis 

      Buitelaar, Paul; Wood, Ian D.; Negi, Sapna; Arcan, Mihael; McCrae, John P.; Abele, Andrejs; Robin, Cécile; Andryushechkin, Vladimir; Ziad, Housam; Sagha, Hesam; Schmitt, Maximilian; Schuller, Björn W.; Sánchez-Rada, J. Fernando; Iglesias, Carlos A.; Navarro, Carlos; Giefer, Andreas; Heise, Nicolaus; Masucci, Vincenzo; Danza, Francesco A.; Caterino, Ciro; Smrž, Pavel; Hradiš, Michal; Povolný, Filip; Klimeš, Marek; Matějka, Pavel; Tummarello, Giovanni (IEEE, 2018-01-25)
      Recently, there is an increasing tendency to embed the functionality of recognizing emotions from the user generated contents, to infer richer profile about the users or contents, that can be used for various automated ...

    • Multimodal emotion recognition for AVEC 2016 challenge 

      Povolny, Filip; Matejka, Pavel; Hradis, Michal; Popkova, Anna; Otrusina, Lubomir; Smrz, Pavel; Wood, Ian; Robin, Cécile; Lamel, Lori (ACM, 2016-10-16)
      This paper describes a systems for emotion recognition and its application on the dataset from the AV+EC 2016 Emotion Recognition Challenge. The realized system was produced and submitted to the AV+EC 2016 evaluation, ...

    • Taxonomy extraction for customer service knowledge base construction 

      Pereira, Bianca; Robin, Cécile; Daudert, Tobias; McCrae, John P.; Mohanty, Pranab; Buitelaar, Paul (Springer, 2019-11-04)
      Customer service agents play an important role in bridging the gap between customers vocabulary and business terms. In a scenario where organisations are moving into semi-automatic customer service, se- mantic technologies ...

    • A term extraction approach to survey analysis in health care 

      Robin, Cécile; Isazad Mashinchi, Mona; Ahmadi Zeleti, Fatemeh; Ojo, Adegboyega; Buitelaar, Paul (European Language Resources Association, 2020-05)
      The voice of the customer has for a long time been a key focus of businesses in all domains. It has received a lot of attention from the research community in Natural Language Processing (NLP) resulting in many approaches ...