Joint multiple dictionary learning for tensor sparse coding

Traditional dictionary learning algorithms are used for finding a sparse representation on high dimensional data by transforming samples into a one-dimensional (1D) vector. This 1D model loses the inherent spatial structure property of data. An alternative solution is to employ Tensor Decomposition for dictionary learning on their original structural form —a tensor— by learning multiple dictionaries along each mode and the corresponding sparse representation in respect to the Kronecker product of these dictionaries. To learn tensor dictionaries along each mode, all the existing methods update each dictionary iteratively in an alternating manner. Because atoms from each mode dictionary jointly make contributions to the sparsity of tensor, existing works ignore atoms correlations between different mode dictionaries by treating each mode dictionary independently. In this paper, we propose a joint multiple dictionary learning method for tensor sparse coding, which explores atom correlations for sparse representation and updates multiple atoms from each mode dictionary simultaneously. In this algorithm, the Frequent-Pattern Tree (FP-tree) mining algorithm is employed to exploit frequent atom patterns in the sparse representation. Inspired by the idea of K-SVD, we develop a new dictionary update method that jointly updates elements in each pattern. Experimental results demonstrate our method outperforms other tensor based dictionary learning algorithms.

Contrastive analysis of keywords in discourses: intégration and integration in French and German discourses about migration

This article suggests a theoretical and methodological framework for a systematic contrastive discourse analysis across languages and discourse communities through keywords, constituting a lexical approach to discourse analysis which is considered to be particularly fruitful for comparative analysis. We use a corpus assisted methodology, presuming meaning to be constituted, revealed and constrained by collocation environment. We compare the use of the keyword intégration and Integration in French and German public discourses about migration on the basis of newspaper corpora built from two French and German newspapers from 1998 to 2011. We look at the frequency of these keywords over the given time span, group collocates into thematic categories and discuss indicators of discursive salience by comparing the development of collocation profiles over time in both corpora as well as the occurrence of neologisms and compounds based on intégration/Integration.

Argument structures in Chinese university students’ argumentative writing: a contrastive study

Situated within a Systemic Functional Linguistics genre paradigm, this study adopted a function-based linguistic approach to examine the argument structures in English writing produced by Chinese university students of English as foreign language (EFL). Their English writing was contrasted with three other sets of argumentative essays in order to explore differences and similarities in the use of argument structures. The four sets of essays were produced by three groups of university students: native English- and Chinese-speaking university students and Chinese university EFL students. Participants’ interviews and questionnaire responses were also collected. The study found that most native English-speaking participants used an analytical arguing strategy, while most Chinese-speaking university participants preferred a hortatory argument structure both in their English and Chinese writing. It was also found that Chinese participants’ English writing was influenced by both English and Chinese.