Direction : Thierry Poibeau and Shravan Vasishth
Natural language understanding is one of the key problems both in cognitive science and in computational linguistics. If both fields do share common methods, the scientific goals are actually different.
In computational linguistics, the last decade has seen the rise of quantitative and statistical methods leading to significant advances in applied sub-fields such as speech recognition, machine translation or information retrieval among others. This line of research has greatly taken advantage of its ability to process very large amounts of data that can be considered close to truly natural data.
Even if this new generation of models is sometimes inspired by linguistic or psycholinguistic theories, it generally brings little or no explanation to the broader question of understanding the natural language competence.
On the other hand, language studies in cognitive science and in psycholinguistics try to better understand the mechanisms underlying natural language (including their neural basis) and more specifically its acquisition by means of experimental investigations. One of the novel aspects in several recent works is to use computational models similar to those used in computational linguistics.
This special issue is dedicated to get a better picture of the relationships between these two research environments. It specifically raises two questions: "what is the potential contribution of computational-linguistics-inspired language modeling to cognitive science" and conversely: "what is the influence of cognitive science in contemporary computational linguistics" ?
The call targets specifically contributions on actual applications of methods from computational linguistics to the modelling of cognitive phenomena and on the other hand on application of cognitive theories to the computational modeling of language. The call addresses all aspects of language modeling from speech to discourse.
Topics include, but are not limited to :
Computational models of natural language acquisition, word clustering and word segmentation
Psycholinguistically motivated phonetic, phonological, morphological syntactic, semantic, pragmatic studies of language
Statistical and probabilistic modeling of factors encouraging one production or interpretation over its competitors
Models of language emergence, change and evolution
Models of language processing and surprisal
Experimental or corpus driven modeling and analysis of language
The call seeks for original papers gathering modeling aspects with empirical or experimental aspects. It also targets theoretical or methodological questions that would allow us to build bridges between the two fields.
