Journal of Computer-Assisted Linguistic Research 2021-12-13T09:51:32+01:00 Carlos Periñán-Pascual Open Journal Systems <p style="text-align: justify; text-justify: inter-ideograph; margin: 0cm 0cm 6.0pt 0cm;"><strong>Journal of Computer-Assisted Linguistic Research (JCLR)</strong> is a double-blind peer-reviewed journal that publishes high-quality scientific articles on linguistic studies where computer tools or techniques play a major role. JCLR aims to promote the integration of computers into linguistic research. In particular, articles in JCLR make a clear contribution to research in which software plays a key role to represent and process written or spoken data. Contributions submitted to JCLR must be in English, but we welcome works about the study of any language. Topics of interest include computational linguistics, text mining, natural language processing, discourse analysis, and language-resource construction, among many others.</p> Analyzing cultural expatriates' attitudes toward “Englishnization” using dynamic topic modeling 2021-12-13T09:51:31+01:00 Ziyuan Zhang Several Japanese multinational corporations (MNCs) have recently adopted an English-only policy known as “Englishnization”.<strong> </strong>This study examines the impact of this policy using computer-assisted text analysis to investigate changes in cultural expatriates’ perceptions of Japanese work practices and values over time. Cultural expatriates are a significant but underexplored outcome of globalization. Despite the recent proliferation of studies on the internationalization of Japanese MNCs, few studies have focused on cultural expatriates' perceptions of corporate language policy in social media texts. This study analyzes a corpus of 208 posts from Rakuten, a Japanese MNC, on Glassdoor from 2009 to 2020. The findings suggest that these posts can be divided into three content groups: the threat of a foreign corporate culture, embracing the Rakuten way, and perceptions of leadership and marginalized status. Further, the posts reveal how Rakuten’s corporate language policy, as an instrument of internal internationalization, impacts external internationalization. The dynamics of “Englishnization’’ reveal a pressing issue facing Rakuten: namely, how to balance multinational cohesion with monolingualism and multiculturalism. This paper aims to demonstrate that dynamic topic modeling could enhance our understanding of the manner in which cultural expatriates and the English-only policy affect the internationalization of Japanese MNCs. It contributes to the literature by examining cultural expatriates’ perceptions of Japanese work practices and values from a diachronic perspective. 2021-12-13T00:00:00+01:00 Copyright (c) 2021 Journal of Computer-Assisted Linguistic Research Indirectly Named Entity Recognition 2021-12-13T09:51:31+01:00 Alexis Kauffmann François-Claude Rey Iana Atanassova Arnaud Gaudinat Peter Greenfield Hélène Madinier Sylviane Cardey <p style="text-indent: 0cm; margin-bottom: 0.42cm; letter-spacing: normal; line-height: 100%; orphans: 0; widows: 0;" lang="en-GB">We define here <em>indirectly named entities</em>, as a term to denote multiword expressions referring to known <em>named entities</em> by means of periphrasis. While named entity recognition is a classical task in natural language processing, little attention has been paid to <em>indirectly named entities</em> and their treatment. In this paper, we try to address this gap, describing issues related to the detection and understanding of <em>indirectly named entities</em> in texts. We introduce a proof of concept for retrieving both lexicalised and non-lexicalised indirectly named entities in French texts. We also show example cases where this proof of concept is applied, and discuss future perspectives. We have initiated the creation of a first lexicon of 712 indirectly named entity entries that is available for future research.</p> 2021-12-13T00:00:00+01:00 Copyright (c) 2021 Journal of Computer-Assisted Linguistic Research Hate speech targets in COVID-19 related comments on Ukrainian news websites 2021-12-13T09:51:32+01:00 Lidiia Melnyk <p dir="ltr"><span lang="EN-US">The research focuses on hate speech in the comments section of Ukrainian news websites. Restricted to solely COVID-19 related comments, it seeks to analyze the development of hate speech rates throughout the pandemic. Using a semi-automated machine-learning-aided approach, the paper identifies hate speech in the comments and defines its main targets. The research shows that a crisis like the COVID-19 pandemic can strengthen existing negative stereotypes and gives rise to new forms of stigmatization against social and ethnic groups.</span></p> 2021-12-13T00:00:00+01:00 Copyright (c) 2021 Journal of Computer-Assisted Linguistic Research