Convergence in word structure: revisiting agglutinative noun inflection in Cappadocian Greek

Cappadocian Greek is reported to display agglutinative inflection in its nominal system, namely, mono-exponential formatives for the marking of case and number, and NOM.SG-looking forms as the morphemic units to which inflection applies. Previous scholarship has interpreted these developments as indicating a shift in morphological type from fusion to agglutination, brought about by contact with Turkish. This study takes issue with these conclusions. By casting a wider net over the inflectional system of the language, it shows that, of the two types of agglutinative formations identified, only one evidences a radical departure from the inherited structural properties of Cappadocian noun inflection. The other, on the contrary, represents a typologically more conservative innovation. The study presents evidence that a combination of system-internal and -external motivations triggered the development of both types, it describes the mechanisms through which the innovation was implemented, and discusses the factors that favoured change.

Academics across borders: narratives of linguistic capital, language competence, and communication strategies

This article reports on a study that examined the personal employment paths of six international academics at a British university. To complement previous accounts of difficult migration, it focuses on the successful experiences of such academics, in particular how proficiency in English facilitated their move into employment in higher education (HE), and the linguistic competences and communication strategies they deploy in their daily activities. The article identifies key factors that have facilitated to their academic achievements and contributes to the understanding of the benefits and consequences of skilled migration. In conclusion, it suggests workplace pedagogy and policy responses that could facilitate other international academics' successful experiences in the UK HE sector.

What is universal and what differs in language development?

Goldin-Meadow (2015) presents an exceptional synthesis of work from studies of children acquiring language under variable circumstances of input or processing abilities. Deaf children who acquire homesign without any well- formed model from which to learn language represent a powerful example. Goldin-Meadow argues that the resilient properties of language that nevertheless emerge include simple syntactic structures, hierarchical organisa- tion, markers modulating the meaning of sentences, and social-communicative functions. Among the fragile or input-dependent properties are the orders that the language follows, the parts into which words are decomposed, and the features that distinguish nominals from predicates. Separation of these two types of properties poses questions concerning the innate constraints on language acquisition (perhaps these equate to the resilient properties) and con‐ cerning the specificity of processes to language (e.g., whether properties such as hierarchical organisation are specific to language or originate in the structure of thought). The study of the resilient properties of human language in the face of adversity and the relation of these properties to the information that is encoded in the human genome represent a research strategy that draws inferences about species universals (properties that all humans share) from data about individual differences (IDs; factors that make humans different from one another). In the following, we suggest three reasons to be cautious about this approach.

Language impairment in a case of a complex chromosomal rearrangement with a breakpoint downstream of FOXP2

BACKGROUND: We report on a young female, who presents with a severe speech and language disorder and a balanced de novo complex chromosomal rearrangement, likely to have resulted from a chromosome 7 pericentromeric inversion, followed by a chromosome 7 and 11 translocation. RESULTS: Using molecular cytogenetics, we mapped the four breakpoints to 7p21.1-15.3 (chromosome position: 20,954,043-21,001,537, hg19), 7q31 (chromosome position: 114,528,369-114,556,605, hg19), 7q21.3 (chromosome position: 93,884,065-93,933,453, hg19) and 11p12 (chromosome position: 38,601,145-38,621,572, hg19). These regions contain only non-coding transcripts (ENSG00000232790 on 7p21.1 and TCONS_00013886, TCONS_00013887, TCONS_00014353, TCONS_00013888 on 7q21) indicating that no coding sequences are directly disrupted. The breakpoint on 7q31 mapped 200 kb downstream of FOXP2, a well-known language gene. No splice site or non-synonymous coding variants were found in the FOXP2 coding sequence. We were unable to detect any changes in the expression level of FOXP2 in fibroblast cells derived from the proband, although this may be the result of the low expression level of FOXP2 in these cells. CONCLUSIONS: We conclude that the phenotype observed in this patient either arises from a subtle change in FOXP2 regulation due to the disruption of a downstream element controlling its expression, or from the direct disruption of non-coding RNAs.

In Support of the Matrix Language Frame Model: Evidence from Igbo-English Intrasentential Codeswitching

This paper explores the morphosyntactic features of mixed nominal expressions in a sample of empirical Igbo-English intrasentential codeswitching data (i.e. codeswitching within a bilingual clause) in terms of the Matrix Language Frame (MLF) model. Since both Igbo and English differ in the relative order of head and complement within the nominal argument phrase, the analysed data seem appropriate for testing the veracity of the principal assumption underpinning the MLF model: the notion that the two languages (in our case Igbo and English) participating in codeswitching do not both contribute equally to the morphosyntactic frame of a mixed constituent. As it turns out, the findings provide both empirical and quantitative support for the basic theoretical view that there is a Matrix Language (ML) versus Embedded Language (EL) hierarchy in classic codeswitching as predicted by the MLF model because both Igbo and English do not simultaneously satisfy the roles of the ML in Igbo-English codeswitching.

Parallel languages in the history of language ideology in Norway and the lesson for Nordic higher education

This chapter compares recent policy on the use of English and Norwegian in Higher Education with earlier policies on the relationship between the two standard varieties of Norwegian, and it charts how and why English became a policy issue in Norway. Based on the experience of over a century of language planning, a highly interventionist approach is today being avoided and language policies in the universities of Norway seek to nurture a situation where English and Norwegian may be used productively side-by-side. However, there remain serious practical challenges to be overcome. This paper also builds on a previous analysis (Linn 2010b) of the metalanguage of Nordic language policy and seeks to clarify the use of the term ‘parallelingualism’.

Igbo-English Intrasentential Codeswitching and the Matrix Language Frame model

This paper uses some data from Igbo-English intrasentential codeswitching involving mixed nominal expressions to test the Matrix Language Frame (MLF) model. The MLF model is one of the most highly influential frameworks used successfully in the study of grammatical aspects of codeswitching. Three principles associated with it, the Matrix Language Principle, the Asymmetry Principle and the Uniform Structure Principle, were tested on data collected from informal conversations by educated adult Igbo-English bilinguals resident in Port Harcourt. The results of the analyses suggest general support for the three principles and for identifying Igbo-English as a ‘classic’ case of codeswitching.

Are Deep Learning Approaches Suitable for Natural Language Processing?

In recent years, Deep Learning (DL) techniques have gained much at-tention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human design or engineering interventions. In addition, DL approaches have achieved some remarkable results. In this paper, we have surveyed major recent contributions that use DL techniques for NLP tasks. All these reviewed topics have been limited to show contributions to text understand-ing, such as sentence modelling, sentiment classification, semantic role labelling, question answering, etc. We provide an overview of deep learning architectures based on Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recursive Neural Networks (RNNs).