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.

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.

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.

The LH5 model for data mining

In the age of E-Business many companies faced with massive data sets that must be analysed for gaining a competitive edge. these data sets are in many instances incomplete and quite often not of very high quality. Although statistical analysis can be used to pre-process these data sets, this technique has its own limitations. In this paper we are presenting a system - and its underlying model - that can be used to test the integrity of existing data and pre-process the data into clearer data sets to be mined. LH5 is a rule-based system, capable of self-learning and is illustrated using a medical data set.

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.

A model for information retrieval driven by conceptual spaces

A retrieval model describes the transformation of a query into a set of documents. The question is: what drives this transformation? For semantic information retrieval type of models this transformation is driven by the content and structure of the semantic models. In this case, Knowledge Organization Systems (KOSs) are the semantic models that encode the meaning employed for monolingual and cross-language retrieval. The focus of this research is the relationship between these meanings’ representations and their role and potential in augmenting existing retrieval models effectiveness. The proposed approach is unique in explicitly interpreting a semantic reference as a pointer to a concept in the semantic model that activates all its linked neighboring concepts. It is in fact the formalization of the information retrieval model and the integration of knowledge resources from the Linguistic Linked Open Data cloud that is distinctive from other approaches. The preprocessing of the semantic model using Formal Concept Analysis enables the extraction of conceptual spaces (formal contexts)that are based on sub-graphs from the original structure of the semantic model. The types of conceptual spaces built in this case are limited by the KOSs structural relations relevant to retrieval: exact match, broader, narrower, and related. They capture the definitional and relational aspects of the concepts in the semantic model. Also, each formal context is assigned an operational role in the flow of processes of the retrieval system enabling a clear path towards the implementations of monolingual and cross-lingual systems. By following this model’s theoretical description in constructing a retrieval system, evaluation results have shown statistically significant results in both monolingual and bilingual settings when no methods for query expansion were used. The test suite was run on the Cross-Language Evaluation Forum Domain Specific 2004-2006 collection with additional extensions to match the specifics of this model.