The LADAL Opening Webinar Series 2021 consists of weekly presentations from a wide range of voices with backgrounds in linguistics, data science, or computational humanities and it covers a variety of topics related to digital handling of language data!
All events take place on Zoom (the Zoom links are provided in the webinar descriptions below and they are announced on Twitter (@slcladal), via the UQ School of Languages and Cultures, and via our collaborators). Once the recordings of the webinars are processed, they are made available on the LADAL YouTube channel.
The LADAL Opening Webinar Series 2021 kicked off with a presentation by Stefan Th. Gries on MuPADRF (Multifactorial Prediction and Deviation Analysis Using Regression/Random Forests) on June 3, 2021, 5pm Brisbane time.
See below for the full list of presentations that are part of the LADAL Opening Webinar Series 2021.
Introducing the VARIENG research unit in Helsinki
Time: 5pm Brisbane, 9am Berlin
Zoom link: https://uqz.zoom.us/j/6388735320
Our presentation falls into three parts. It begins with Terttu’s brief introduction to the Research Unit for Variation, Contacts and Change in English (VARIENG), its past, present and future, and its current open-access resources. In addition, the talk presents two short cases studies touching on the methodology of corpus linguistics from different perspectives. The first one (by Turo) discusses how the availability of massive text archives may hold great promise for corpus linguistic work, but they may also present considerable methodological challenges for users (see e.g. Hiltunen, McVeigh, and Säily 2017). In focus here are some specific problems related to the diachronic British Library Newspapers database, and how those problems might be addressed in the context of register analysis and the study of linguistic variation. The second case study (by Aatu) looks into the role of text length in register-internal variation by analyzing a big data sample of social media comments. Different registers seem to exhibit different kinds of internal functional variation by text length (as shown by the illustration).
Terttu is the Director of the VARIENG Research Unit. Her research interests include historical sociolinguistics, variation studies, corpus linguistics and digital humanities. She is one of the designers and compilers of the Helsinki Corpus of English Texts and of the Corpus of Early English Correspondence.
Turo Hiltunen works as senior lecturer in English at the Department of Languages, University of Helsinki, Finland. His research interests include corpus linguistics and register analysis, and the grammatical and phraseological variation of scientific English past and present, compilation of specialised corpora, and democratisation in the context of parliamentary discourse.
Aatu Liimatta is researching register variation on social media for his PhD. His interests include register and functional variation, computational methods, and big linguistic data.
An Introduction to AntConc 4: Developing a general-purpose corpus toolkit for a broad user base
Time: 5pm Brisbane, 9am Berlin
Zoom link: https://uqz.zoom.us/j/6388735320
AntConc is a widely used desktop corpus tool that has been downloaded over 2.5 million times since its first release in the early 2000s. Today, it is used by researchers, teachers, and learners in over 140 countries and its tutorial videos have been viewed over 500,000 times. AntConc has a relatively easy-to-use design and works especially well with small corpora of under a few million words. However, it begins to struggle when processing larger corpora and offers relatively few statistics for advanced analysis. In this talk, I will introduce a new version of AntConc (version 4.0) that has been built from the ground up to addresses these limitations. The new version includes features that allow the software to be easily extended, and it produces results in a way that allows for smooth data interoperability with other tools and scripts. I will also discuss various issues that need to be considered when developing software for a broad user base, which should be of interest to both users and developers of software tools.
Laurence is Professor of Applied Linguistics at the Faculty of Science and Engineering, Waseda University, Japan. He has a BSc degree (Mathematical Physics) from the University of Manchester, UK, and MA (TESL/TEFL) and PhD (Applied Linguistics) degrees from the University of Birmingham, UK. He is the current Director of the Center for English Language Education in Science and Engineering (CELESE), which runs discipline-specific language courses for the 10,000 students of the faculty. His main research interests are in corpus linguistics, educational technology, and English for Specific Purposes (ESP) program design and teaching methodologies. He received the National Prize of the Japan Association for English Corpus Studies (JAECS) in 2012 for his work in corpus software tools design.
Doing diachronic linguistics with distributional semantic models in R
Time: 5pm Brisbane, 9am Berlin
Zoom link: https://uqz.zoom.us/j/6388735320
Computational linguistics offers promising tools for tracking language change in diachronic corpora. These tools exploit distributional semantic models, both old and new. DSMs tend to perform well at the level of lexical semantics but are more difficult to fine-tune when it comes to capturing grammatical meaning.
I present ways in which the above can be improved. I start from well-trodden methodological paths implemented in diachronic construction grammar: changes in the collocational patterns of a linguistic unit reflect changes in meaning/function; distributional word representations can be supplemented with frequency-based methods. I move on to show that when meaning is apprehended with predictive models (e.g. word2vec), one can trace semantic shifts with greater explanatory power than with count models. Although this idea may sound outdated from the perspective of NLP, it actually goes great ways from the viewpoint of theory-informed corpus linguistics.
I illustrate the above with several case studies, one of which involves complex locative prepositions in the Corpus of Historical American English. I conclude my talk by defending the idea that NLP, with its focus on computational efficiency, and corpus-linguistics, with its focus on tools that maximize data inspection, have much to gain from getting closer.
Guillaume is Associate Professor of Linguistics at Paris 8 University, France, and a researcher at the MoDyCo laboratory of the University of Paris Nanterre. He is the recipient of a 5-year honorary position at the Institut Universitaire de France, a division of the French Ministry of Higher Education that distinguishes university professors for their research excellence.
Guillaume’s research interests are at the crossroads of cognitive linguistics and corpus linguistics. More specifically, he uses corpus-linguistics and statistical techniques to test usage-based hypotheses. He has published on modality, evidentiality, and intensification from a construction-grammar perspective.
In 2017 Guillaume published a reference textbook on corpus linguistics with R: Corpus Linguistics and Statistics with R (New York: Springer). The same year, he opened a research blog: Around the word, A corpus linguist’s notebook, where he has since recorded reflections and experiments on his practice as a usage-based corpus linguist (https://corpling.hypotheses.org/).
Strategic targeting of rich inflectional morphology for linguistic analysis and L2 acquisition
Note: This talk was originally scheduled for September 2.
Time: 5pm Brisbane, 9am Berlin
Zoom link: https://uqz.zoom.us/j/6388735320
Many languages have rich inflectional morphology signaling grammatical categories such as case, number, tense, etc. Rich morphology presents a challenge for L2 learners because even a basic vocabulary of a few thousand words can entail mastery of over 100,000 word forms. However, only a handful of the potential forms of a given word occur frequently, while the remainder are rare. Access to digital corpora makes it possible to determine which forms of any given word are of highest frequency, as well as what grammatical and collocational contexts motivate those few frequent forms, facilitating strategically focused language learning tools. Corpus analysis of the frequency distributions of inflectional forms provide linguists with added insights into the function of languages. The results achieved primarily by using correspondence analysis of Russian material are potentially portable to any language with rich inflectional morphology.
In the Cold War era, Laura Janda combined study of Slavic linguistics at Princeton and UCLA with US-government-funded adventures as an exchange student behind the Iron Curtain in countries that have since changed their names: USSR, Czechoslovakia, and Yugoslavia. After over two decades at the University of Rochester and UNC-Chapel Hill, she moved to the University of Tromsø in 2008. Laura Janda was an early adopter of Cognitive Linguistics in the 1980s and has explored quantitative methods since 2007. Her research focuses primarily on the morphology of Slavic languages, with various admixtures (North Saami, conlangs, political discourse).
Language variation and change in eighteenth-century publications and publishing networks
Time: 5pm Brisbane, 9am Berlin
Zoom link: https://uqz.zoom.us/j/6388735320
This talk introduces our new project, Rise of Commercial Society and Eighteenth-Century Publishing, by discussing its premises and a pilot study. Combining historical sociolinguistics with intellectual history, book history and data science, the project studies the eighteenth-century print media and publishing networks that enabled the rise of commercial society and its conceptualization in the eighteenth-century Anglophone world. The chief focus is on Scottish, transatlantic and French influences on British print media.
A key innovation in our project is that we connect the study of eighteenth-century publishing networks with that of language variation and change to gain results that are of interest to both linguists and historians. To do this, we link enriched bibliographic metadata with full-text sources for historical-sociolinguistic analysis. Our main interest is in charting the use and spread of new vocabulary in the networks to better understand how the discourse develops over time. Instead of focusing on well-known authors, we will identify influencers in a data-driven way; these can also be printers or publishers. By combining network analysis with text mining and social metadata on the actors, we are able to conduct large-scale analyses of how linguistic and stylistic changes spread across social groups in eighteenth-century public discourse.
Project website: https://blogs.helsinki.fi/c18-publishing/
Tanja Säily is a tenure-track assistant professor in English language at the University of Helsinki. Her research interests include corpus linguistics, digital humanities, historical sociolinguistics, and linguistic productivity. She is also interested in the social embedding of language variation and change in general, including gendered styles in the history of English and extralinguistic factors influencing language change. Her overarching aim is to develop new ways of understanding language variation and change, often in collaboration with experts from other fields. Her current project combines historical sociolinguistics, intellectual history, book history and data science to analyse eighteenth-century publications and publishing networks.
Git and GitHub/Gitlab for versioning and collaborating
Time: 5pm Brisbane, 9am Berlin
Zoom link: https://uqz.zoom.us/j/6388735320
Git is a tool for versioning of and collaborating on any text-based file. Widely used in software development, it is now adopted in many different settings, from document versioning to data analysis management. It is also at the centre of major platforms like GitHub and GitLab, used by millions to share and collaborate on code and documents.
In this workshop, you will learn about:
The main commands used in a git workflow
How to publish your work online
How to collaborate on a GitHub repository
If you would like to follow along, please do the following before attending:
Install Git on your computer (here are OS-specific instructions)
Stéphane has worked for the last 10 years at the University of Queensland (UQ). After completing a master’s degree in plants science and ecology in France, he worked in research around the topic of sustainable agriculture. In 2018, a drastic move to a Technology Trainer position at the Library allowed him to share data analysis best practice skills, and promote Open Source tools for research. He is motivated by the principles of Open Science and the opportunities an increasingly collaborative research ecosystem offers.
Reproducibility, Replicability, and Robustness
Time: 5pm Brisbane, 9am Berlin
Zoom link: https://uqz.zoom.us/j/6388735320
Scientific progress has long rested on the often unstated assumptions that research is reproducible (i.e., independent analysts can recreate the results claimed by the original authors by using the original data and analysis techniques), replicable (the results claimed by analysts extends beyond the original data to some wider population or phenomenon), and robust (the findings reported by analysts are not ovely sensitive to assumptions in their model). Recently, however, there have been growing concerns about the extent to which current research practices can meet these assumptions. In this talk, I’ll present a high-level discussion of these issues, defining the key terms, demonstrating how and why questions have been raised about why work may not be as reproducible, replicable, or robust as we may wish, and offer some tentative suggestions for and examples of improving the reproducibility, replicability, and robustness of linguistic research in the future.
Joseph Flanagan is a University Lecturer in Languages/English Studies at the University of Helsinki where he is also supervisor for the doctoral program in Philosophy, Arts, and Society. Joe’s research interests primarily center on issues related to English phonetics and phonology, reproducible research, and the digital humanities. Teaching-wise, he is especially interested in exploring how digital technology can enhance student learning.
Joe has presented, given talks, and co-organized several workshops on reproducibility and replicability in (corpus) linguistics at international conferences such as the 6th Meeting of the International Society for the Linguistics of English (ISLE6) and ICAME42.
Phylogenetic comparative methods: What all the fuss is about, and how to use them in everyday research
Time: 5pm Brisbane, 9am Berlin
Zoom link: https://uqz.zoom.us/j/6388735320
In this talk, we attempt two tasks. Firstly, we dispel some of the mystery around phylogenetic comparative methods and highlight their fundamental relationship to matters of enduring concern in linguistic typology. Secondly, we aim to show how linguists can carry out essential tasks using phylogenetic methods, easily.
We begin by laying out the historical commonalities between typology and comparative biology, and the breakthrough insight that makes phylogenetic comparative methods distinct. This is followed by an overview of some fundamental phylogenetic concepts and tools. Finally, we illustrate these with a typological case study from the Pama-Nyungan languages. Our talk is accompanied by online interactive materials, demonstrating how phylogenetic comparative methods can be incorporated into everyday typological workflows.
Jayden is a postdoctoral researcher in linguistics at the CNRS Dynamics of Language Lab, Lumière University Lyon 2. He recently completed his PhD at The University of Queensland as a member of the Ancient Language Lab. Jayden specialises in historical linguistics and typology, particularly using phylogenetic and quantitative methods.
Erich is British Academy Global Professor of linguistics at the Surrey Morphology Group, University of Surrey, UK, and director of the Ancient Language Lab at the University of Queensland. His research focuses on linguistic evolution and typology, especially in the domains of morphology and phonology. Erich also specialises in the Tangkic languages of Queensland and the phonologies of Australian Indigenous languages.
MuPADRF (Multifactorial Prediction and Deviation Analysis Using Regression/Random Forests)
Recording link: https://www.youtube.com/watch?v=cLocET9CC-E&t=253s
In this talk, Stefan gave a brief and relatively practical introduction to an approach called MuPDAR(F) (for Multifactorial Prediction and Deviation Analysis using Regressions/Random Forests) that he developed (see Gries and Deshors (2014), Gries and Adelman (2014) for the first applications). The main part of the talk involved using a version of the data in Gries and Adelman (2014) to exemplify how this protocol works and how it can be done in R. Second, Stefan discussed a few recent extensions proposed in Gries and Deshors (2020) and Gries (n.d.), which have to do with
how to deal with situations with more than two linguistic choices,
how predictions are made, and
how deviations are quantified.
Finally, he briefly comment on exploring individual variation among the target speakers (based on Gries and Wulff (2021)).
Stefan Th. Gries is full professor at the University of California, Santa Barbara (UCSB), as well as Honorary Liebig-Professor and Chair of English Linguistics at the Justus-Liebig-Universität Giessen. Stefan has held several prestigious visiting professorships at top universities and, methodologically, he is a quantitative corpus linguist at the intersection of corpus linguistics, cognitive linguistics, and computational linguistics. Stefan has applied a variety of different statistical methods to investigate a wide range of linguistic topics and much of his work involves the open-source software R. Stefan has produced more than 200 publications (articles, chapters, books, and edited volumes), he is an active member of various editorial boards as well as academic societies.
The Australian Text Analytics Platform (ATAP) and the Language Technology and Data Analysis Laboratory (LADAL) - building computational humanities infrastructures: experiences, problems, and potentials
Recording on YouTube: https://www.youtube.com/watch?v=qGIGCubyJs0
This talk introduces the Language Technology and Data Analysis Laboratory (LADAL) which is a computational humanities resource infrastructure maintained by the School of Languages and Cultures at the University of Queensland. The talk will also provide information about its relations to the Australian Text Analytics Platform (ATAP) which represents an effort to promote text analytics in Australia and to make resources for using text analytics available to a wider community of researchers.
Martin is a language data scientist with a PhD in English linguistics who has specialized in corpus linguistics and quantitative, computational analyses of language data. Martin is a Lecturer in Applied Linguistics at the University of Queensland, Australia where he has been establishing the Language Technology and Data Analysis Laboratory (LADAL) and he holds an additional part-time Associate Professorship in the AcqVA-Aurora Center at the Arctic University of Norway in Tromsø.
Michael is Professor of Linguistics and a Fellow of the Australian Academy of the Humanities. His research interests lie primarily in the field of pragmatics, the science of language-in-use. He works with recordings and transcriptions of naturally occurring spoken interactions, as well as data from digitally-mediated forms of communication across a number of languages. An area of emerging importance in his view is the role that language corpora can play in the humanities and social sciences more broadly. He has been involved in the establishment of the Australian National Corpus and the Language Technology and Data Analytics Lab, and is currently leading the establishment of a national language data commons.
Field-based methods for collecting quantitative data
Recording on YouTube: https://youtu.be/BGa0gkkkWsc
Shana Poplack has set benchmarks for the development of corpora since the early 1980s. Poplack (2015, p. 921) maintains that the “gold standard remains the (standard sociolinguistic-style) … corpus”. The aim of producing corpora using these principles is to avoid the ‘cherry picking’ approach which dominates much of the theoretical literature. Poplack and her team have created the Ottawa-Hull Corpus which consists of 3.5 million words of informal speech data. This corpus is enormous and beyond the capabilities of a single linguist in a small language community. This talk offers suggestions for corpus development in the field that follow Poplack’s principles, but also shows where compromises can be made. I discuss the method developed during the Gurindji Kriol project called ‘peer elicitation’. It supplements Poplack’s gold standard of naturally occurring speech with semi-formal elicitation to ensure sufficient data for quantitative analyses.
Felicity Meakins is an ARC Future Fellow in Linguistics at the University of Queensland and a CI in the ARC Centre of Excellence for the Dynamics of Language. She is a field linguist who specialises in the documentation of Australian Indigenous languages in the Victoria River District of the Northern Territory and the effect of English on Indigenous languages. She has worked as a community linguist as well as an academic over the past 20 years, facilitating language revitalisation programs, consulting on Native Title claims and conducting research into Indigenous languages. She has compiled a number of dictionaries and grammars of traditional Indigenous languages and has written numerous papers on language change in Australia.
Text Crunching Center (TCC): Data-driven methods for linguists, social science and digital humanities
Recording on YouTube: https://youtu.be/2_l3ViTzBOM
This talk introduces the Text Crunching Centre (TCC) which is a Computational Linguistics and Digital Humanities service hosted at the University of Zurich, and a collaboration partner of LADAL. We present a selection of our case studies using text analytics, from cognitive linguistics, social, political and historical studies. We show how stylistics, document classification, topic modelling, conceptual maps, distributional semantics and eye-tracking can offer new perspectives. Our case studies include language and age, learner language, the history of medicine, democratisation, religion, and attitudes to migration. We conclude with an outlook to the future of text analytics.
Gerold Schneider is a Senior Lecturer, researcher and computing scientist at the department of Computational Linguistics at the University of Zurich, Switzerland. His doctoral degree is on large-scale dependency parsing, his habilitation on using computational models for corpus linguistics. His research interests include corpus linguistics, statistical approaches, Digital Humanities, text mining and language modeling. He has published over 100 articles on these topics. He has published a book on statistics for linguists (Schneider and Lauber 2019), and a book on digital humanities is under way. His Google scholar page can be accessed here.
Corpus-based media linguistics: A case study of linguistic diversity in Australian television
In this golden age of Indigenous television (Sebbens, n.d.), it is important to analyze Indigenous-authored drama series, so that we can move beyond ‘a deficit perspective’ (Charity Hudley, Mallison, and Bucholtz 2020, 216) in relation to mediated linguistic diversity. This talk presents a corpus linguistic case study of Australian Aboriginal English (AAE) lexis as present in three such Indigenous-authored television series: Redfern Now, Cleverman, and Mystery Road. For television viewers, mediated AAE can be an important source of information, especially if they do not regularly interact with Aboriginal and/or Torres Strait Islander people. This would be the case for many Australians, and even more so for international viewers of Australian television series. All three analyzed series were exported overseas and thus have both Australian and international audiences. Using lexical profiling analysis (AntWordProfiler; Anthony (2013)) in combination with qualitative concordance analysis, the talk identifies and compares the use of AAE lexis across the three series. Analysis of frequency and distribution will pinpoint words that appear to be particularly significant lexical resources in mediated AAE. The talk will be framed through the notion of diversity, as conceptualised in relation to television series.
Monika is Professor of Linguistics at the University of Sydney and Director of the Sydney Corpus Lab. Her research uses corpus linguistic methodologies across a variety of fields, including media linguistics, discourse analysis and sociolinguistics. She has a particular interest in the linguistic expression of emotion and opinion, with a focus on English. Monika is the author or co-author of six books and two short volumes as well as numerous journal articles and book chapters. She has co-edited several edited volumes and special issues of journals, most recently Corpus approaches to telecinematic language (International Journal of Corpus Linguistics 26/1, 2021) and Corpus linguistics and Education in Australia (Australian Review of Applied Linguistics 43/2, 2020). She is on the steering committee of the Asia Pacific Corpus Linguistics Association and tweets @corpusling.
Recycle, relax, repeat: Advantages of Bayesian inference in comparison with frequentist methods
Recording on YouTube: https://youtu.be/vpsJj7Nkgw4
The slides for Natalia’s talk are available here. An Open Science Foundation (OSF) repository containing the R code and data used for the case study in this talk are available here. Here is a link to the GitHub repo of Shravan Vasishth (Professor for Psycholinguisics at the University of Potsdam) with additional resources on Bayesian statistics.
Bayesian inference is becoming increasingly popular in linguistic research. In this talk I will compare frequentist (maximum likelihood) and Bayesian approaches to generalized linear mixed-effects regression, which is de facto the standard method for testing linguistic hypotheses about linguistic variation. The main advantages of Bayesian inference include an opportunity to test the research hypothesis directly, instead of trying to reject the null hypothesis. One can also use information from previous research as priors for subsequent models, which helps to overcome the recent crisis of reproducibility. This also enables one to use smaller samples. It helps to solve such problems as overfitting, data separation and convergence issues, which often arise when one fits generalized mixed-effect models with complex structure. These advantages will be illustrated by a multifactorial case study of help + (to-)infinitive in US magazines, as in the example These simple tips will help you (to) survive the Zombie apocalypse.
Natalia is a linguist working at the Max Planck Institute for Psycholinguistics in Nijmegen. Her main research interests are cognitive and functional linguistics, pragmatics, typology, corpora and data science. She obtained her PhD at KU Leuven in 2011 and got her habilitation qualification at Leipzig University in 2019 with a thesis Towards a theory of communicative efficiency. In addition to papers on causatives, differential case marking, politeness, word order variation and other linguistic topics, Natalia is the author of a best-selling statistical manual How to Do Linguistics with R (Levshina 2015).
Gathering data and creating online experiments with jsPsych and JATOS
Recording on YouTube: https://youtu.be/7-9WRYpXEtE
Resources: The materials, including scripts, files, etc., can be accessed via and downloaded from this GitHub repository.
Web-based studies are an increasingly popular and attractive alternative to lab- and field-based studies, enabling remote data collection for a rapidly growing variety of (psycho-)linguistic methods ranging from acceptability judgment tasks and self-paced reading to interactive group designs and the Visual World paradigm. Most platforms for building and hosting online studies are proprietary and subscription-based (e.g., Gorilla, Pavlovia, and FindingFive), but there also exist various free, open-source tools for writing and managing studies on your own server. This talk gives a practical introduction to building and hosting studies using jsPsych (De Leeuw 2015) and JATOS (Lange, Kühn, and Filevich 2015), by demonstrating a speedrun of the entire process: from an empty file in a code editor, to distributing URLs to participants.
Myrte Vos (she/they) is a doctoral research fellow at the Arctic University of Norway (Tromsø). They’re supposed to be studying incremental processing of aspect and modality in English, but got sidetracked by figuring out how to do that using webcam-based eye tracking.
Going down the Reproducible Research pathway: You have to begin somewhere, right?
Resources: The materials that are mentioned in this presentation can be accessed via the following url: https://www.qcif.edu.au/training/training-courses/
The idea that you can duplicate an experiment and get the same conclusion is the basis for all scientific discoveries. Reproducible research is data analysis that starts with the raw data and offers a transparent workflow to arrive at the same results and conclusions. However not all studies are replicable due to lack of information on the process. Therefore, reproducibility in research is extremely important.
Researchers genuinely want to make their research more reproducible, but sometimes don’t know where to start and often don’t have the available time to investigate or establish methods on how reproducible research can speed up every day work. We aim for the philosophy “Be better than you were yesterday”. Reproducibility is a process, and we highlight there is no expectation to go from beginner to expert in a single workshop. Instead, we offer some steps you can take towards the reproducibility path following our Steps to Reproducible Research self paced program.
Amanda Miotto is an eResearch Analyst for Griffith University and QCIF. She started off in the field of Bioinformatics and learnt to appreciate the beauty of science before discovering the joys of coding. She is also heavily involved in Software Carpentry, Hacky Hours and ResBaz, and has developed on platforms around HPC and scientific portals.
Julie Toohey is a Library Research Data Management Specialist at Griffith University Library. Julie has an extensive career in academic libraries and is passionate about research data management practices. Previously, Julie co-facilitated the Australian National Data Services 23 Things (research data) Health and Medical Data Community series and is currently a member of the QULOC Research Support Working Party. Julie works closely with Griffith eResearch Services delivering education awareness programs around managing research data, reproducible research and working with sensitive data. Julie has co-authored several research data related publications with Griffith researchers and eResearch Services partners.
Neurocognitve effects of bilingual experience
Recording on YouTube: https://youtu.be/wVBkUSPQFUw
Much research over the past two decades shows that bilingualism affects brain structure, function, and potentially domain-general cognition (see e.g., Bialystok 2017; Pliatsikas 2019). The specificity of these effects, however, has become the subject of significant debate in recent years, in large part due to variability of findings across studies (see Leivada et al. 2020 for review). In this talk, we will introduce our research programs within the Psycholinguistics of Language Representation (PoLaR) lab that addresses the juxtaposition of data and argumentation. Our work is guided by the principle that although bilingual effects are existent, they are conditional. In other words, bilingualism per se is not a sufficient condition for relevant effects on neurocognition. We will review our work that is generally designed to test the hypothesis that specific experience-based factors (EBFs) variably affect neural activity and plasticity in brain regions and pathways implicated in language- and executive control across the lifespan. We present results from a series of MRI studies showing a specificity of neural adaptations to different EBFs (DeLuca, Rothman, and Pliatsikas 2019; DeLuca et al. 2019, 2020) in younger adults. We will also present data from older adults, showing similar EBF effects in healthy cognitive ageing (Voits, Robson, and Pliatsikas, n.d.) and with mild cognitive impairment (Voits et al., n.d.). EBFs related to duration of bilingual language use correlate to neurocognitive adaptations suggesting increased efficiency in language control, whereas those related to extent of additional language use correlate with adaptations suggesting increased control demands. Considered together, these data suggest that the brain strives to be maximally effective and efficient in language processing and control, which in turn affects domain-general cognitive processes proportionally to degree of engagement with bilingual experiences. The work in older populations leads to the conclusion that degree of engagement with bilingualism is a catalyst for cognitive/brain reserve and thus has some real-world benefits in aging.
Vincent is Associate Professor in the Neurocognition of Bilingualism and co-director of the Psycholinguistics of Language Representation (PoLaR) lab in the AcqVa Aurora Centre at UiT-The Arctic University of Norway. His research is focused on how different aspects of bilingual language experience variably impact brain structure, function, and several cognitive processes. His work focuses on how these neural and cognitive adaptations dynamically shift over time and with changes to patterns of language use.
Toms is a Postdoctoral Researcher at UiT the Arctic University of Norway. He is affiliated with the Psycholinguistics of Language Representation (PoLaR) lab and the AcqVA Aurora Center in the Department of Language and Culture at UiT. His work is primarily focused on investigating the effects of bilingualism on neurocognition, with a particular interest in examining bilingualism as a contributing factor to cognitive and brain reserves in the later years of life.
Jason is Professor of Linguistics at UiT the Arctic University of Norway and Senior Researcher in Cognitive Science at Universidad Nebrija (Spain). He is deputy director of the AcqVA Aurora Center at UiT, where he co-leads the center’s theme/concentration on the Neurocognition of Bilingualism. Professor Rothman also co-directs the center’s Psycholinguistics of Language Representation (PoLaR) lab. A linguist by training, he has worked extensively on language acquisition, linguistic processing and language-associated links to domain general neurocognition across the lifespan of varioustypes of bi-/multilingual populations. He is founding editor of the journal Linguistic Approaches to Bilingualism and serves as executive editor of the book series Studies in Bilingualism.
A gentle introduction to networks
Recording on YouTube: https://youtu.be/QW0lVaHcNX4
Linguists have adopted several methods from data science, but network analysis has been used rather less than others even though it is a useful tool. This presentation will introduce the basics of using network analysis, discussing the types of problems for which the method is useful, the kinds of data which are amenable to analysis, and the graphical outputs which can be achieved. These points will be illustrated with several examples from different areas of linguistic research, as well as with an example with data concerning a social network.
Simon Musgrave was a lecturer in the Linguistics program at Monash University until the end of 2020. His research covers various areas in linguistics and sociolinguistics, linked by the themes of the use of computational tools in linguistic research and the relationship between Linguistics and Digital Humanities, an interest continued in his current work in the Linguistic Data Commons of Australia project.
(Semi-)Automated speech recognition using ELPIS
Recording on YouTube: https://youtu.be/9YjZH4lBco0
Speech recognition (ASR) technologies can be useful to accelerate transcription of language recordings, and to provide new insights into corpora. ASR tools are available now for hundreds of languages from commercial providers and can be trained for languages that are not commercially supported. However, there are significant hurdles to using ASR tools, from the preparation of data through to the training of the systems. This presentation introduces the motivations and process of co-designing Elpis, a speech recognition system built to be usable by ordinary working linguits. We discuss some examples of using Elpis with a range of low-resource language corpora, and how the co-design process can be used to benefit other language technologies.
Ben is the project manager of CoEDL’s Transcription Acceleration Project (TAP). TAP brings cutting-edge language technology within reach of people working with some of the world’s oldest languages. A major focus of TAP is the development of user-friendly speech recognition tools. Ben’s previous experience with Aboriginal and Torres Strait Islander language resource development has resulted in apps and websites galore. Highlights include the Iltyem-iltyem sign language database and website, and the Gambay First Languages Map, showing the hundreds of languages in Australia.
Janet is a Professor in Human Centred Computing at the University of Queensland and leads the Future Technologies Thread of the ARC Centre of Excellence for the Dynamics of Language (CoEDL). She has 30 years’ experience in cross-disciplinary research and teaching, including artificial intelligence, language technologies and social robotics, leading teams that span engineering, humanities, social sciences and neuroscience.
Ben and Janet currently teach a cross disciplinary course Voyages in Language Technologies"* that introduces computing students to the diversity of the world’s languages, and state-of-the-art tools for deep learning and other analysis techniques for working with language data.
Adding social information to societal big data?
Recording on YouTube: https://youtu.be/kfb7xLC2nBs
Societal big data today provides a large source of language data in naturalistic settings. Such data have substantially enlarged pools of evidence in various fields in social sciences and the humanities. In linguistics, one direct impact has been the emergence of computational sociolinguistics, a field that intersects sociolinguistics with computational techniques.
However, social big data have one major limitation, at least when it comes to computational sociolinguistics. This limitation, quite surprisingly, concerns the lack of social background information. Researchers have no direct access to background information (author’s gender, social layer, education, occupation, etc.), and it is difficult to combine evidence from social media with rich social information for the simple reason that such information is not available for proprietary reasons. If some social information is available, it is often self-reported and therefore prone to inaccuracies. Or, it is ethically unsustainable to link big language data with socio-cultural information (cf. the Cambridge Analytica scandal).
This talk introduces a method that is being developed in my SOCOS research group. The method builds on social network theory and utilizes freely available interaction data. These data can be quantified easily through a set of algorithms and used as proxies for social parameters. One downside is that it requires some degree of technical competence to extract these data, which can easily be accomplished through interdisciplinary partnerships in digital humanities environments.
Mikko Laitinen is Professor of English Language at the University of Eastern Finland. He obtained PhD in 2007 and has been a member of the Academy of Finland Center of Excellence in Research Unit for Variation, Contacts and Change (VARIENG) since 2000. He previously worked as Professor of English at Linnaeus University, where he is one of the two founding members of the Center of Data Intensive Sciences and Applications (DISA), a multidisciplinary big data research consortium that consists of scholars in the humanities, computer scientists, mathematicians, and social scientists. His research focuses on the role social networks in language variation and change, computational sociolinguistics, and digital humanities.
Working with Social Media Data
Recording on YouTube: https://youtu.be/QVwWPfxE93U
Social media platforms, built on the web and the internet, are now just part of life for most of us. The pervasive communication and connection the web and social media enables is a potentially rich source of data for research - but there are pitfalls for the unprepared. This talk provides a survey of the web as a source of data for research projects, including considerations of privacy, ethics and governance; the technical approaches to data collection; and an overview of approaches to analysing such data.
Sam is a developer/data scientist at Queensland University of Technology’s Digital Observatory (yes, the slash is important!). He has a PhD under examination in machine learning for medical image analysis and a commercial background in software development for text analytics algorithms and products. At the Digital Observatory, Sam helps researchers deal with the challenges of collecting, modelling and analysing digital data to address their research questions. He is particularly interested in both the web as a medium, and in the development of computationally assisted methods for bridging the qualitative and quantitative divide.
Data-driven learning for younger learners: Boosting schoolgirls’ knowledge of passive voice constructions for STEM education
Recording on YouTube: https://youtu.be/JlHchgpzO3o
This paper explores how corpus technology and DDL pedagogy can support secondary schoolgirls’ reporting of an observed science experiment through a written research report, focusing particularly on how corpora were used to develop receptive and productive knowledge of passive voice constructions. A pre-test of the grammaticality of passive constructions was conducted, alongside a diagnostic pre-instruction written report requiring the retelling of an observed science experiment were collected from 60 Year 9-10 girls at a high school in Australia. During a full 10-week term, students were given guided individual homework tasks and short in-class pair/group DDL activities focusing on passive voice constructions, using freely available online corpus applications such as SketchEngine. Following this treatment, a post-test was conducted while an additional written research report was collected. Questionnaire and interview data was also collected to determine the perceptions of younger female learners and their teachers regarding their engagement with corpora and DDL for improving knowledge and use of passive constructions over time. The data suggest that while the DDL treatment did not result in increased receptive knowledge of the grammaticality of passive voice constructions, their productive use of the passive was significantly improved. Moreover, when students were given the opportunity to use corpora to check their intuitions in the post-test, they produced accurate responses over 75% of the time. Qualitative stakeholder perceptions of improved disciplinary linguistic knowledge, increased data management skills, and positive engagement with “science” were also found in the survey/interview data, although a number of challenges at the technical and conceptual levels for DDL still remain.
Peter is Senior Lecturer in Applied Linguistics in the School of Languages and Cultures at the University of Queensland. Before he joined UQ, Peter was assistant professor at the Centre for Applied English Studies (CAES) at the University of Hong Kong. His areas of research and supervisory expertise include corpus linguistics and the use of corpora for language learning (known as data-driven learning), as well as English for General and Specific Academic Purposes. Peter is the author of the monograph Learning the language of Dentistry: Disciplinary corpora in the teaching of English for specific academic purposes which is part of Benjamins’ Studies in Corpus Linguistics series (with Lisa Cheung, published 2019), as well as the edited volumes Data Driven Learning for the Next Generation: Corpora and DDL for Pre-tertiary Learners (published 2019) and Referring in a second language: Reference to person in a multilingual world (with Jonathon Ryan, published 2020 with Routledge). Peter is also currently serving as the corpus linguistics section editor for Open Linguistics - an open access linguistics journal from De Gruyter, and he is on the editorial board of Applied Corpus Linguistics, a new journal covering the direct applications of corpora to teaching and learning.
Text classification for automatic detection of hate speech, counter speech, and protest events
Recording on YouTube: https://youtu.be/2oLJNYl_Ipw
Text Mining Tutorials: https://tm4ss.github.io
R package for active learning experiments: https://github.com/tm4ss/tmca.classify
Experiment code for reproducing the experiments from the Journal Article Proportional Classification Revisited: https://github.com/tm4ss/tmca.classify-experiments
Social sciences have opened up to text mining, i.e., a set of methods to automatically identify semantic structures in large document collections. However, the methods have often been limited to a statistical analysis of textual data, strongly limiting the scope of possible research questions. The more complex concepts central to the social sciences such as arguments, frames, narratives and claims still are mainly studied using manual content analyses in which the knowledge needed to apply a category (i.e. to “code”) is verbally described in a codebook and implicit in the coder’s own background knowledge. Supervised machine learning provides an approach to scale-up this coding process to large datasets. Recent advantages in neural network-based natural language processing allow for pretraining language models that can transfer semantic knowledge from unsupervised text collections to specific automatic coding problems. With deep learning models such as BERT automatic coding of context-sensitive semantics with substantially lowered efforts in training data generation comes within reach to content analysis. The talk will introduce to the applied usage of these technologies along with two interdisciplinary research projects studying hate speech and counter speech in German Facebook postings, and information extraction for the analysis of the coverage of protest events in local news media.
Dr. Gregor Wiedemann is working as Senior Researcher Computational Social Science at the Leibniz Institute for Media Research │ Hans Bredow Institute (HBI). Since September 2020, he heads the Media Research Methods Lab (MRML). His current work focuses on the development of methods and applications of natural language processing and text mining for empirical social and media research. Gregor Wiedemann studied political science and computer science in Leipzig and Miami, USA. In 2016 he received his doctorate from the Department of Computer Science at the University of Leipzig for his thesis on automation of discourse and content analysis using text mining and machine learning methods. Afterwards he worked as a postdoc in the NLP group of Computer Science Department at the University of Hamburg. Among other things, the resulting works are concerned with unsupervised information extraction to support investigative research in unknown document collections (see newsleak.io) and with the detection of hate and counter-speech in social media.
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