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"Dalam Bahasa Inggris untuk Akademik, pengetahuan tentang ungkapan atau idiom sangat dibutuhkan untuk meningkatkan kemampuan menulis dan berpikir kritis siswa. Namun banyak guru maupun instructur Bahasa tidak menyadari hal ini dan bahkan tidak tahu bagaimana menyertakannya dalam diskusi yang menyangkut aspek leksikan, terutama dalam kaitannya dengan bahasa dan budaya. Studi ini bertujuan untuk meneliti tentang frekuensi ungkapan academik yang digunakan oleh orang Amerika dan orang Inggris melalui analisa korpus. Diskusi diangkat berdasarkan analisa frekuensi ungkapan per seribu kata yang muncul pada BC dan COCA"

"The borrowing of foreign lexicon in Indonesian is well documented. However, to date, research on Korean loanwords has remained unavailable. Against the backdrop of the Korean Wave (hallyu), this paper discusses the phonological adaptation of Korean loanwords in Indonesian and the way these loanwords contribute to the Indonesian lexical landscape. By collecting data on Korean culture from a selection of Instagram and Twitter (now X) accounts from the Indonesian community, our corpus shows that besides nouns, Korean loanwords also include adjectives and verbs. We identified 52 loanwords related to the domain of popular culture, including film, music, and food. The different phonological systems of Korean and Indonesian determine the assimilation processes in the Indonesian vocabulary. Since this paper involves big data stored in a corpus, it has the capacity to provide new insight in the ways Korean loanwords and their phonological structure are integrated in Indonesian and become linguistically acceptable."

""Language acquisition occupies a central place in the study of human cognition, and research on how we learn language can be found across many disciplines, from developmental psychology and linguistics to education, philosophy and neuroscience. The investigation of a complex phenomenon like language acquisition naturally requires insights, tools, and methods from many disciplines, yet it is still relatively rare to find studies that combine multiple approaches. This volume brings together leading researchers in cognitive psychology, corpus research, developmental psychology, linguistics, and natural language processing to discuss opportunities and challenges of combining multiple approaches to language learning. The peer-reviewed chapters in this volume are written in an accessible and engaging fashion. Together, they provide the reader with a panorama of the exciting research currently being conducted at the intersection of experimental, computational and corpus-based approaches to language learning. chology and linguistics to education, philosophy and neuroscience. The investigation of achology and linguistics to education, philosophy and neuroscience. The investigation of a"--"The special issue contains conceptual and empirical papers combining perspectives from a range of disciplines including second language research, corpus linguistics, computational linguistics, and cognitive psychology. Papers (a) discuss recent work and trends, (b) outline opportunities and challenges of working across disciplinary and methodological boundaries as well as (c) propose directions for future research. The special issue is inspired by a symposium organized by the ESRC Centre for Corpus Approaches to Social Science at Lancaster University and the journal Language Learning (2015)"--"

"Recognition of handwritten mathematical expressions has been an important topic for many researchers for decades. It remains one of the most challenging and exciting areas in pattern recognition. In the recognition process of offline handwritten mathematical expressions, segmentation is the most important process. Problems in ambiguities of identifying superscript and subscript in complex offline mathematical expressions remain one of the most important problem. To the best of our knowledge little work has been done in the segmentation of offline handwritten mathematical expressions with respect to superscript and subscript. In this paper an efficient segmentation technique for superscript, subscript and main characters within offline handwritten mathematical expressions has been proposed. This technique is based on the generation of predictions for superscript, subscript and main characters within handwritten mathematical expressions, which helps for the reconstruction of mathematical expressions during the recognition process with their spatial interrelationship. The proposed system was conducted as an experiment with a database of 300 samples of scanned mathematical expressions that comprised 2,000 symbols out of which there were 31 different types of Mathematical Symbols. The classification of the elements was carried out by the K-NN-classifier based on density features. This experiment shows remarkable results."