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1 Introduction The past two decades have seen a surge of interest in two crucial research topics: the study of mutual interactions of the affective and linguistic domains and the exploration of language processing in the bilingual mind and brain. Even a cursory glance at the titles of thematic conferences, colloquia, symposia, workshops and discussion panels, as well as of new scientific journals that have emerged over the recent years testifies to the popularity of these subject areas. This popularity becomes all the more evident in the steady rise of individual

1 Introduction We describe our endeavour to build natural language processing (NLP) tools for a group of west Ugandan Bantu languages, Runyakitara. Orthographies for these languages were formalized no more than 150 years ago, and we have found that spelling errors are common in written Runyakitara, even by students in higher education and ‘professional’ users such as journalists. We therefore plan to build a spell-checker, RunyaSpeller, for Runyakitara which will help to improve writing accuracy, underpin general literacy, and support government policies in

a language . Even well-formulated psycholinguistic and neurolinguistic models often suffer from underspecification with many parameters omitted and many more never empirically estimated. Implemented computational models suffer less from the underspecification problem, but still have many issues with free parameters ( Howes et al. 2009 ) and researcher degrees of freedom ( Simmons et al. 2011 ). Previously, we presented a computational model of language processing based on the interaction of weighted prominence features ( Alday et al. 2014 ). While our models

of choices is inherently probabilistic, that is to say in each situation, various choices are more or less likely to be selected by a speaker” ( McEnery and Wilson 2001 : 111–112). Not only did this definition of variation foreshadow advances in statistical machine translation ( Manning and Schütze 1999 ); it furthermore emphasized the need for psycholinguistic models of human language processing to take variation into consideration. It is somewhat surprising, therefore, that psycholinguistics, the field that seeks to understand how language is processed in and

1 Introduction In this paper, I describe the relationship of the field of typology to computational linguistics and natural language processing . Those latter two terms are sometimes used interchangeably to describe the field concerned with the processing of human language by computers. If a distinction is drawn between them, computational linguistics is used to describe research interested in answering linguistic questions using computational methodology, while natural language processing describes research on automatic processing of human language for

DOI 10.1515/cog-2013-0004   Cognitive Linguistics 2013; 24(1): 115 – 134 Patrizia Noel Aziz Hanna Language processing and the evolution of rhythmic patterns: Asymmetries in binary stress systems* Abstract: Rhythmic stress is assigned automatically in everyday speech. Usually, it is produced without conscious planning of which syllables have to be stressed. However, the ‘grammaticalisation’ of rhythmic patterns is the result of language processing. It is the outcome of a selective process which is proposed to lead to a preference or dispreference for

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