Cross-language Text Classification with Convolutional Neural Networks From Scratch

Cross language classification is an important task in multilingual learning, where documents in different languages often share the same set of categories. The main goal is to reduce the labeling cost of training classification model for each individual language. The novel approach by using Convolutional Neural Networks for multilingual language classification is proposed in this article. It learns representation of knowledge gained from languages. Moreover, current method works for new individual language, which was not used in training. The results of empirical study on large dataset of 21 languages demonstrate robustness and competitiveness of the presented approach

A method for the analysis of data from online educational research

The intention of this article is to provide an alternative method of data analysis for online learning and VLE related research that is essentially paper based. The article describes the use of a paper-based method for data analysis of online learning type research that involves the collection and collation of electronic (and possibly also paper based) data. This method partly builds on the work of Tyler (2001) and has been used on research projects that investigated online learning as a method for widening participation (Hramiak, 2001a, 2002a) and also on a project that involved the e-professional development of staff at a Further Education (FE) college (Hramiak, 2004). Starting with the raw data sets, a distillation process for the data is described. This is followed by an explanation of how the data sets are examined for common themes. One of the major challenges facing the e-learning researcher is how to analyze the electronic data such as discussion board messages and emails, and then how to understand the implications of this analysis for teaching and learning. Such analysis enables researchers to act upon the situation in order to improve it for the learners, as well as for themselves (Lally, 2000). This is particularly challenging when the messages are not only numerous, in the region of hundreds or even thousands, for a specific research study, but also because they can be both very complicated and very lengthy. Although tools for analyzing communication patterns have been developed in other disciplines, for example in applied linguistics, they are generally based upon the analysis of large bodies of text. They also involve relatively complex and cumbersome methods, and they are not designed for action research use in the immediacy of particular teaching and learning situations (Lally) such as those for which this article is aimed at – namely those in which students/participants are constantly messaging in real time synchronously and asynchronously. Moreover, such tools are not intentionally designed to analyze dynamic, ongoing collaborative and social situations where knowledge is actively being co-constructed by the participants (Lally)

SURVEY OF E-MAIL CLASSIFICATION: REVIEW AND OPEN ISSUES

Email is an economical facet of communication, the importance of which is increasing in spite of access to other approaches, such as electronic messaging, social networks, and phone applications. The business arena depends largely on the use of email, which urges the proper management of emails due to disruptive factors such as spams, phishing emails, and multi-folder categorization. The present study aimed to review the studies regarding emails, which were published during 2016-2020, based on the problem description analysis in terms of datasets, applications areas, classification techniques, and feature sets. In addition, other areas involving email classifications were identified and comprehensively reviewed. The results indicated four email application areas, while the open issues and research directions of email classifications were implicated for further investigation