Lexical simplification of scientific terms represents a unique challenge due to the lack of a standard parallel corpora and fast rate at which vocabulary shift along with research. We introduce SimpleScience, a lexical simplification approach for scientific terminology. We use word embeddings to extract simplification rules from a parallel corpora containing scientific publications and Wikipedi...

This study examined the capacity of text-based online chat to promote learners’ noticing of their problematic language productions and of the interactional feedback from their interlocutors. In this study, twelve ESL learners formed six mixed-proficiency dyads. The same dyads worked on two spot-thedifference tasks, one via online chat and the other through face-to-face conversation. Stimulated ...

Text classification (TC) or text categorization task is assigning a document to one or more predefined classes or categories. A common problem in TC is the high number of terms or features in document(s) to be classified (the curse of dimensionality). This problem can be solved by selecting the most important terms. In this study, an automatic text summarization is used for feature selection. S...

This paper suggests that in dispersed settings presence and awareness are continuously produced and reproduced by human agents through communication genres. Specifically, the paper argues that presence and awareness are two interrelated concepts. More specifically, it proposes four communication genres people intentionally or unintentionally draw upon in dispersed settings in order to create aw...

This study investigated the effectiveness of incidental focus on form in promoting second language development in text-based live chats. Sixteen college-level Taiwanese English language learners were partnered with American college students to complete two communicative tasks via synchronous chats on Instant Messenger. Language-related episodes (LREs) were identified and used as a basis for ind...

The effect of selecting varying numbers and kinds of features for use in predicting category membership was investigated on the Reuters and MUC-3 text categorization data sets. Good categorization performance was achieved using a statistical classifier and a proportional assignment strategy. The optimal feature set size for word-based indexing was found to be surprisingly low (10 to 15 features...

Text detection from videos is a well known research area. Especially the detection of static superimposed text such as captions has been researched successfully, but makes many assumptions that question the applicability of those algorithms for moving scene text. In this dissertation, I propose a scene text area detection approach that includes a simple key frame extraction, feature extraction,...

With the development of the web, large numbers of documents are available on the Internet. Digital libraries, news sources and inner data of companies surge more and more. Automatic text categorization becomes more and more important for dealing with massive data. However the major problem of text categorization is the high dimensionality of the feature space. At present there are many methods ...