Stasinos Konstantopoulos


Unravelling Names of Fictional Characters
Katerina Papantoniou | Stasinos Konstantopoulos
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


Argument extraction for supporting public policy formulation
Eirini Florou | Stasinos Konstantopoulos | Antonis Koukourikos | Pythagoras Karampiperis
Proceedings of the 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities


Task-Driven Linguistic Analysis based on an Underspecified Features Representation
Stasinos Konstantopoulos | Valia Kordoni | Nicola Cancedda | Vangelis Karkaletsis | Dietrich Klakow | Jean-Michel Renders
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

In this paper we explore a task-driven approach to interfacing NLP components, where language processing is guided by the end-task that each application requires. The core idea is to generalize feature values into feature value distributions, representing under-specified feature values, and to fit linguistic pipelines with a back-channel of specification requests through which subsequent components can declare to preceding ones the importance of narrowing the value distribution of particular features that are critical for the current task.


A Quantitative and Qualitative Analysis of Nordic Surnames
Eirini Florou | Stasinos Konstantopoulos
Proceedings of the 18th Nordic Conference of Computational Linguistics (NODALIDA 2011)


An Embodied Dialogue System with Personality and Emotions
Stasinos Konstantopoulos
Proceedings of the 2010 Workshop on Companionable Dialogue Systems

Learning Language Identification Models: A Comparative Analysis of the Distinctive Features of Names and Common Words
Stasinos Konstantopoulos
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

The intuition and basic hypothesis that this paper explores is that names are more characteristic of their language than common words are, and that a single name can have enough clues to confidently identify its language where random text of the same length wouldn't. To test this hypothesis, n-gramm modelling is used to learn language models which identify the language of isolated names and equally short document fragments. As the empirical results corroborate the prior intuition, an explanation is sought for the higher accuracy at which the language of names can be identified. The results of the application of these models, as well as the models themselves, are quantitatively and qualitatively analysed and a hypothesis is formed about the explanation of this difference. The conclusions derived are both technologically useful in information extraction or text-to-speech tasks, and theoretically interesting as a tool for improving our understanding of the morphology and phonology of the languages involved in the experiments.


Adaptive Natural Language Interaction
Stasinos Konstantopoulos | Athanasios Tegos | Dimitrios Bilidas | Ion Androutsopoulos | Gerasimos Lampouras | Colin Matheson | Olivier Deroo | Prodromos Malakasiotis
Proceedings of the Demonstrations Session at EACL 2009

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An Intelligent Authoring Environment for Abstract Semantic Representations of Cultural Object Descriptions
Stasinos Konstantopoulos | Vangelis Karkaletsis | Dimitris Bilidas
Proceedings of the EACL 2009 Workshop on Language Technology and Resources for Cultural Heritage, Social Sciences, Humanities, and Education (LaTeCH – SHELT&R 2009)


Learning Computational Grammars
John Nerbonne | Anja Belz | Nicola Cancedda | Hervé Déjean | James Hammerton | Rob Koeling | Stasinos Konstantopoulos | Miles Osborne | Franck Thollard | Erik F. Tjong Kim Sang
Proceedings of the ACL 2001 Workshop on Computational Natural Language Learning (ConLL)