A trilingual dictionary Yilumbu–French–English : an ongoing project

In this article, an account is given of the planning of a trilingual dictionary Yilumbu– French–English. The focus is on the target user, the purpose, nature and typology of the planned dictionary. Attention is also paid to some macro- and microstructural issues. For example, all types of lexical items, including multiword lexical items, are given lemma status. Moreover all items are included according to the word tradition and on account of their usage frequency in the corpus. Apart from these aspects, types of dialectal forms as well as the type of special-field lexical items are also discussed. From a microstructural point of view, this article investigates different kinds of data types to be considered for inclusion in complex articles in particular. User-friendliness parameters and innovative access structure procedures also come into play.Cet article rend compte de la planification d'un dictionnaire trilingue yilumbu–français– anglais. Le centre d'intérêt réside au niveau du public cible, l'objectif, la nature et la typologie du dictionnaire proposé. Une attention est aussi accordée à quelques problèmes macro- et microstructurels. Par exemple, tous les types d'items lexicaux, y compris les items lexicaux formés de plusieurs mots, reçoivent le statut de lemme. En outre, tous les termes sont inclus selon la tradition dumot et sur la base de leur fréquence d'emploi dans le corpus. Hormis ces aspects, les types de formes dialectales ainsi que le type d'items lexicaux de spécialité sont également discutés. D'un point de vue microstructurel, cet article explore différents types de données à considérer pour inclusion dans les articles complexes en particulier. Les paramètres de clarté et de lisibilité ainsi que des procédés propre à la structure d'accès sont également pris en compte

A Trilingual Dictionary Yilumbu– French–English: An Ongoing Project

In this article, an account is given of the planning of a trilingual dictionary Yilumbu– French–English. The focus is on the target user, the purpose, nature and typology of the planned dictionary. Attention is also paid to some macro- and microstructural issues. For example, all types of lexical items, including multiword lexical items, are given lemma status. Moreover all items are included according to the word tradition and on account of their usage frequency in the corpus. Apart from these aspects, types of dialectal forms as well as the type of special-field lexical items are also discussed. From a microstructural point of view, this article investigates different kinds of data types to be considered for inclusion in complex articles in particular. User-friendliness parameters and innovative access structure procedures also come into play. Keywords: dictionary, lexicography, dictionary plan, metalexicography, target group, gabon, source language, target language, english, french, yilumb

Autoencoding the Retrieval Relevance of Medical Images

Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a $n/p/n$ autoencoder ($p\!<\!n$). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.Comment: To appear in proceedings of The 5th International Conference on Image Processing Theory, Tools and Applications (IPTA'15), Nov 10-13, 2015, Orleans, Franc

NITELIGHT: A Graphical Tool for Semantic Query Construction

Query formulation is a key aspect of information retrieval, contributing to both the efficiency and usability of many semantic applications. A number of query languages, such as SPARQL, have been developed for the Semantic Web; however, there are, as yet, few tools to support end users with respect to the creation and editing of semantic queries. In this paper we introduce a graphical tool for semantic query construction (NITELIGHT) that is based on the SPARQL query language specification. The tool supports end users by providing a set of graphical notations that represent semantic query language constructs. This language provides a visual query language counterpart to SPARQL that we call vSPARQL. NITELIGHT also provides an interactive graphical editing environment that combines ontology navigation capabilities with graphical query visualization techniques. This paper describes the functionality and user interaction features of the NITELIGHT tool based on our work to date. We also present details of the vSPARQL constructs used to support the graphical representation of SPARQL queries

Investigating Semantic Alignment in Character Learning of Chinese as a Foreign Language: The Use and Effect of the Imagery Based Encoding Strategy

For learners of Chinese as a foreign language (CFL), character learning is frustrating. This research postulated that this difficulty may mainly come from a lack of semantic understanding of character-denoted meanings. Language theories support that when a learner’s semantic meaning increases, the orthographic structures that represent the underlying meanings also improve. This study aimed to reveal CFL learners’ cognitive abilities and processes in visual-semantic learning of Chinese characters. Particularly, this study investigated the process by which English-speaking adolescent CFL learners, at the beginning to intermediate level, made mental images of character-denoted meanings to visually encode and retrieve character forms. Quantitative and qualitative data were gathered from image making questionnaires, writing, and reading tests, after learning characters in three commonly-used teaching methods (i.e., English, pictorial, and verbal). The data were analyzed based on a triangulation of the literature from Neuro-Semantic Language Learning Theory, scientific findings in cognitive psychology, and neuroscience. The study found that participants’ semantic abilities to understand character-denoted meanings emerged, but were still restricted in familiar orthographic forms. The use of the imagery strategy as a semantic ability predicted better performances, most evidently in writing; however, the ability in using the imagery strategy to learn characters was still underdeveloped, and needed to be supported with sufficient contextual information. Implications and further research in visual-semantic learning and teaching characters were suggested