Online talks 2025-6

Online Talks: 2025-6

Series Theme: Bias and AI in Corpus Linguistics

• 11 November 2025: Niall Curry, MMU: ‘GenAI and corpus linguistics: Questions of alignment, application, and literacy
• 18 November 2025: Mick O’Donnell, Universidad Autónoma de Madrid: 'Is there a role for linguists in the future of AI?
• 2pm 27 January 2026: Laurence Anthony, Waseda University: 'AI-Enhanced Concordancing with Word and Sentence Embeddings'
• 2pm 3 February 2026: Sally Hunt, Rhodes University: 'CADS & Representations of Gender in Children’s Literature (and a little bit of AI) (View the recording)
• 2pm 17 March 2026: Matthew Shardlow, Manchester Metropolitan University: 'Deanthropomorphisation of scientific reporting of LLMs ' (View the recording // Download the slides)
• 2pm 28 April 2026: Matteo Fuoli, University of Birmingham: ‘Scaling discourse annotation with large language models’ (View the slides)

Forthcoming Autumn 2026:

• Sylvia Jaworska, University of Reading (Date/Time TBC!)

Abstracts and Bios for Presenters in 2025-6

Matteo Fuoli, University of Birmingham: ‘Scaling discourse annotation with large language models’

From annotation to automation: Metaphor identification using large language models
Metaphor is a pervasive feature of discourse, yet it remains particularly difficult to quantify reliably due to its highly context-sensitive nature. For this reason, most existing studies rely on manual corpus annotation, a process that is both time-consuming and tedious. But what if we could use large language models (LLMs) to at least partially automate this task? In this talk, we present a study in which we tested a range of LLMs and three different methodological approaches to LLM-assisted metaphor identification. Our findings show that state-of-the-art closed-source models can achieve high levels of accuracy, with fine-tuning yielding a median F1 score of 0.79. A comparison of human and model outputs further reveals that most disagreements are systematic, reflecting well-known grey areas and enduring conceptual challenges in metaphor theory.

17th March 2026

Matthew Shardlow, Manchester Metropolitan University

Deanthropomorphisation of scientific reporting of LLMs

Reference list:

Shardlow, M. and Przybyła, P., 2024. Deanthropomorphising NLP: can a language model be conscious?. PloS one, 19(12), p.e0307521.

Shardlow, M., Williams, A., Roadhouse, C., Ventirozos, F. and Przybyła, P., 2025, July. Exploring supervised approaches to the detection of anthropomorphic language in the reporting of NLP venues. In Findings of the Association for Computational Linguistics: ACL 2025 (pp. 18010-18022).

Shardlow, M., Williams, A., Roadhouse, C., Ventirozos, F.K. and Przybyła, P., 2025, September. Learn, Achieve, Predict, Propose, Forget, Suffer: Analysing and Classifying Anthropomorphisms of LLMs. In Proceedings of Interdisciplinary Workshop on Observations of Misunderstood, Misguided and Malicious Use of Language Models (pp. 86-94).

Dorielle Lonke, Jelke Bloem, and Pia Sommerauer. 2025. AnthroSet: a Challenge Dataset for Anthropomorphic Language Detection. In Proceedings of Interdisciplinary Workshop on Observations of Misunderstood, Misguided and Malicious Use of Language Models, pages 27–39, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.

Muhammad Owais Raza and Areej Fatemah Meghji. 2025. Anthropomorphizing AI: A Multi-Label Analysis of Public Discourse on Social Media. In Proceedings of Interdisciplinary Workshop on Observations of Misunderstood, Misguided and Malicious Use of Language Models, pages 64–73, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.

Inie, N., Druga, S., Zukerman, P. and Bender, E.M., 2024, June. From" AI" to Probabilistic Automation: How Does Anthropomorphization of Technical Systems Descriptions Influence Trust?. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 2322-2347).

Inie, N., Zukerman, P. and Bender, E.M., 2026. De-anthropomorphizing “AI”: From wishful mnemonics to accurate nomenclature. First Monday.

Cheng, M., Gligorić, K., Piccardi, T. and Jurafsky, D., 2024, March. AnthroScore: A computational linguistic measure of anthropomorphism. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 807-825).

DeVrio, A., Cheng, M., Egede, L., Olteanu, A. and Blodgett, S.L., 2025, April. A taxonomy of linguistic expressions that contribute to anthropomorphism of language technologies. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1-18).

Talk 2pm 3rd February 2026:

Sally Hunt, Rhodes University, South Africa

Talk 2pm 27th January 2026:

Professor Laurence Anthony

AI-Enhanced Concordancing with Word and Sentence Embeddings

Concordancing is a cornerstone of corpus-based research and inductive language learning, but it places heavy demands on users to craft precise queries and to interpret large numbers of results presented in relatively simple formats. This talk explores how recent advances in transformer-based word, sentence, and sentence-fragment embeddings can be integrated with traditional concordancing to address these limitations. Embeddings enable more flexible, context-sensitive searches that go beyond exact word matching, allowing users to retrieve semantically related examples without complex query design. In addition, similarity-based sorting and clustering of concordance lines offers new ways to organize large result sets, making salient patterns easier to identify. Using examples from the BE06 and AmE06 corpora, the talk demonstrates applications such as semantic and synonym searches within and across language varieties. The proposed approach enhances the interpretability, efficiency, and pedagogical value of concordance data, expanding the role of concordancing in corpus linguistics and language education.

Bio

Laurence Anthony is Professor of Applied Linguistics at the Faculty of Science and Engineering, Waseda University, Japan. He holds a BSc in Mathematical Physics from the University of Manchester, UK, and MA (TESL/TEFL) and PhD (Applied Linguistics) degrees from the University of Birmingham, UK. His research interests include language data science, educational technology, corpus linguistics, and science communication.

Talk 18th November 2025

Dr Mick O'Donnell

Is there a role for linguists in the future of AI?

In the 20th century, most research in AI involved human experts hand-crafting rules to drive “intelligent” decision-making systems. Within NLP, linguists provided grammar rules and lexicons to drive syntactic parsing. Other linguists worked out the rules which govern coherent conversation. The role of the computer scientists was to code programmes to apply these rules efficiently for the analysis or generation of human language.

From around 1980, small groups of researchers started experimenting with an alternative approach, involving machine learning. On the surface, these approaches did away with the need for linguists to hand-craft linguistic knowledge, rather the programmes themselves were tasked to analyse language and produce a functional model of language.  This work has culminated in recent years with AI systems such as ChatGPT, which to a large degree reflect human-like intelligent behaviour.

This talk will argue that through the development of LLMs, linguists have played an important role. At one level, linguists have been employed to prepare training data given to LLMS, so that the LLM can replicate human linguistic analysis. At a deeper level, linguists have defined the overall architecture of the LLMs, specifying the various components which work together to allow the overall system to function, such as analytical, generative, conversational and planning components.

The talk will stress that the AI is not a finished product, but will be continuously re-architectured to improve performance, and linguists are needed for this to be done effectively. In particular,  AIs currently lack a deep understanding of how human activity is organised in terms of cultures and institutions, and we linguists need to work out how AIs can be trained to function as culturally aware beings.

Bio:

Mick O'Donnell is a lecturer in English Studies at the Universidad Autónoma de Madrid. He has been working in various areas of NLP since the 1980s, in text generation, parsing, and dialogue management. He is best known for the annotation tools he has developed, including Systemic Coder (1992), RSTTool (1997), and UAM Corpustool (2008). His interests currently concern applying NLP tools to explore second language acquisition

Talk 11^th November 2025:

Dr Niall Curry

GenAI and corpus linguistics: Questions of alignment, application, and literacy

The role of Generative AI (GenAI) in the research process has emerged as a key topic of critical debate in corpus linguistics. For every proposed boon that the use of GenAI heralds, there is a complementary bane, and while both the body of research on Gen AI use and research in corpus linguistics using GenAI continues to grow, we do not appear to have arrived at any clear consensus surrounding its affordances and limitations. In this talk, I draw on some recent work that addresses the issue of GenAI use in corpus linguistics research. The talk spotlights some of the key ideas emerging from this work, addressing questions of GenAI literacy, ethics, knowledge-making, and the relevance of large language models for corpus linguistics research. Through this exploration of emergent key issues, I reflect on the ‘goodness of the fit’ of GenAI for our research activity and consider the research areas in which the application of GenAI may be a) ineffective, b) antithetical to our research agenda, or c) pose some opportunity for research and knowledge-making.

Bio

Dr Niall Curry is a UKRI Metascience AI Fellow and Reader in Languages and Linguistics at the School of English, at Manchester Metropolitan University. His work addresses issues in applied linguistics, with specific interest in corpus linguistics, contrastive linguistics, discourse analysis, and language pedagogy. His most recent work addresses the use of Artificial Intelligence in applied linguistics, climate discourses across languages and cultures, corpus linguistics for materials development, and studies of public-oriented research communication. He is Series Co-Editor of the Routledge Applied Corpus Linguistics and Routledge Corpus Linguistics Guides book series, Section Editor of Elsevier Encyclopedia of Language and Linguistics, a Fellow of The Royal Society of Arts, and an Associate Fellow of the Global China Academy. For more information about Niall, his publications, and his projects, visit: linktr.ee/niallrcurry