Analysing Lexical Semantic Change with Contextualised Word Representations

This paper presents the first unsupervised approach to lexical semantic change that makes use of contextualised word representations. We propose a novel method that exploits the BERT neural language model to obtain representations of word usages, clusters these representations into usage types, and measures change along time with three proposed metrics. We create a new evaluation dataset and show that the model representations and the detected semantic shifts are positively correlated with human judgements. Our extensive qualitative analysis demonstrates that our method captures a variety of synchronic and diachronic linguistic phenomena. We expect our work to inspire further research in this direction.Comment: To appear in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL-2020

Academic literacies twenty years on: a community-sourced literature review

In 1998, the paper ‘Student writing in higher education: an academic literacies approach’ by Mary Lea and Brian Street reinvigorated debate concerning ‘what it means to be academically literate’ (1998, p.158). It proposed a new way of examining how students learn at university and introduced the term ‘academic literacies’. Subsequently, a body of literature has emerged reflecting the significant theoretical and practical impact Lea and Street’s paper has had on a range of academic and professional fields. This literature review covers articles selected by colleagues in our professional communities of the Association for Learning Development in Higher Education (ALDinHE), BALEAP the global forum for English for Academic Purposes (EAP) professionals, and the European Association of Teachers of Academic Writing (EATAW). As a community-sourced literature review, this text brings together reviews of wide range of texts and a diverse range of voices reflecting a multiplicity of perspectives and understandings of academic literacies. We have organised the material according to the themes: Modality, Identity, Focus on text, Implications for research, and Implications for practice. We conclude with observations relevant to these themes, which we hope will stimulate further debate, research and professional collaborations between our members and subscribers

Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network

Language in social media is extremely dynamic: new words emerge, trend and disappear, while the meaning of existing words can fluctuate over time. Such dynamics are especially notable during a period of crisis. This work addresses several important tasks of measuring, visualizing and predicting short term text representation shift, i.e. the change in a word's contextual semantics, and contrasting such shift with surface level word dynamics, or concept drift, observed in social media streams. Unlike previous approaches on learning word representations from text, we study the relationship between short-term concept drift and representation shift on a large social media corpus - VKontakte posts in Russian collected during the Russia-Ukraine crisis in 2014-2015. Our novel contributions include quantitative and qualitative approaches to (1) measure short-term representation shift and contrast it with surface level concept drift; (2) build predictive models to forecast short-term shifts in meaning from previous meaning as well as from concept drift; and (3) visualize short-term representation shift for example keywords to demonstrate the practical use of our approach to discover and track meaning of newly emerging terms in social media. We show that short-term representation shift can be accurately predicted up to several weeks in advance. Our unique approach to modeling and visualizing word representation shifts in social media can be used to explore and characterize specific aspects of the streaming corpus during crisis events and potentially improve other downstream classification tasks including real-time event detection