The Application and Effectiveness of Prototype Theory in The New Oxford English-Chinese Dictionary

The New Oxford English-Chinese Dictionary (NOECD) is a bilingualized dictionary based on the New Oxford Dictionary of English (NODE), which is likely the first dictionary to claim explicitly to put prototype theory into dictionary making. Assessments of the effectiveness of this move vary, but so far, no empirical study has been conducted to examine it. This paper reports two studies of the application of prototype theory in NOECD. The first regards the use of the theory in defining and sense organising, and the second concerns users’ perception of the effectiveness of the organisation of sense. The first study is qualitative, and it examines how prototype theory is exhibited in defining and sense organising. The second study is empirical and consists of a test of dictionary users’ vocabulary retention and some follow-up questions. In this second study, it was found that the prototype strategy adopted by NOECD, of identifying a core sense and grouping subsenses around it, contributed little to improving user efficiency in memorising word meanings. Several possible reasons are proposed to account for the ineffectiveness: the influence of L1, limitations of prototype theory, users’ inadequate dictionary skills and others. It is concluded that these challenges could be addressed by compiling nation-specific dictionaries for specific users. Dictionary training should be an inseparable part of language learning to improve users’ dictionary skills and awareness.

Laplace Graph Embedding Class Specific Dictionary Learning for Face Recognition

The sparse representation based classification (SRC) method and collaborative representation based classification (CRC) method have attracted more and more attention in recent years due to their promising results and robustness. However, both SRC and CRC algorithms directly use the training samples as the dictionary, which leads to a large fitting error. In this paper, we propose the Laplace graph embedding class specific dictionary learning (LGECSDL) algorithm, which trains a weight matrix and embeds a Laplace graph to reconstruct the dictionary. Firstly, it can increase the dimension of the dictionary matrix, which can be used to classify the small sample database. Secondly, it gives different dictionary atoms with different weights to improve classification accuracy. Additionally, in each class dictionary training process, the LGECSDL algorithm introduces the Laplace graph embedding method to the objective function in order to keep the local structure of each class, and the proposed method is capable of improving the performance of face recognition according to the class specific dictionary learning and Laplace graph embedding regularizer. Moreover, we also extend the proposed method to an arbitrary kernel space. Extensive experimental results on several face recognition benchmark databases demonstrate the superior performance of our proposed algorithm.

Low-Rank Optimization Dictionary Training for Image Classification

Bag-of-words model has been extremely popular in image categorization. The method of constructing the dictionary is important. In this paper a category constrained low-rank optimization dictionary training approach is proposed for the dictionary construction. Through the low-rank optimization, the rank of the coefficient matrix constructed by same category images is minimized. Experimental results show that the proposed method can obtain better performance on two standard image databases (Caltech-101 and Caltech-256) than not employing the category constrained low-rank optimization.