• SIBling — reframing how semantic change is measured: Lead author, ACL 2024 Main — introduced a multidimensional framework and computational toolkit for evaluating lexical semantic change across Sentiment, Intensity, and Breadth, foregrounding connotational dimensions of meaning change that are often overlooked. [paper · code]
  Uptake: Applied and extended in computational linguistics to track semantic change in Japanese economic news, and in social psychology to track concept creep in mental health concepts.

• LSC-Eval — evaluates semantic change methods under controlled conditions: Lead author, ACL Findings 2025 — developed an evaluation framework using LLM generated diachronic synthetic corpora to test whether computational methods can detect specific dimensions of semantic change, using the mental health domain as a case study. [paper · code]

• SenseRel — evaluating denotational and connotational sense relations: Joint first author, ACL 2026 Main — Co-developed a novel gold standard benchmark for testing whether AI language models represent both referential and evaluative relations between word senses. Developed during my Change is Key! research internship. [Paper]

• Computational social science applications: Lead author, ICWSM 2026 — a study of how women are dehumanized compared to men in incel discourse across five dimensions: negative evaluation, moral disgust, animal likeness, mind denial, and agency denial. [paper · code]

• Invited talks: Presented my work on computational methods for tracking meaning change in the mental health domain at Utrecht University, the University of Gothenburg, and the National Research Council Canada — spanning the Netherlands, Sweden, and Canada.

• Awards and research support: Australian Government Research Training Program Scholarship; Change is Key! international research internship; ARC Concept Creep dissemination funding; University of Melbourne Hallmark Research Initiative support for Fighting Harmful Online Communication.

I study how word meanings change as concepts move across scientific writing, news media, and general language. My work focuses especially on language with social, cultural, or psychological significance, including mental health concepts and contested social terms. I ask how these terms extend into new contexts, shift in emotional intensity, and acquire more positive or negative associations. To examine these questions, I combine NLP methods with theory from social psychology, computational social science, and corpus linguistics, using historical text corpora, contextual embeddings, lexical resources, large language models, and statistical modeling.

Research streams

My work clusters around three overlapping streams: (1) diachronic lexical semantic change and evaluation, (2) conceptual change in psychology, and (3) computational social science projects on socially contested language.

• Diachronic lexical semantic change and evaluation
  I develop computational methods and evaluation resources for tracing different kinds of diachronic lexical semantic change in historical text corpora, with applications in psychology, computational social science, linguistics, and NLP method evaluation. Contributions include:

  • SIBling, a multidimensional framework for modeling whether words become broader, more emotionally intense, or acquire more positive or negative connotations over time.
  • LSC-Eval, an evaluation framework that uses LLM-generated historical datasets in experimental settings to test whether semantic change methods are sensitive to specific kinds of change.
  • A threshold-calibrated sense tracking pipeline for estimating the prevalence of a word’s senses in historical corpora. It touches on LSC interpretability by showing how LSC scores may not reflect sense change.
  • SenseRel, a sense-level benchmark for modeling semantic relations between word senses, connecting denotational semantic change types (e.g., metaphor, metonymy) with connotational dimensions of meaning (e.g., valence and arousal). We evaluate how well LLMs and fine-tuned models capture these relations.

• Conceptual change in psychology and mental health language

  I study how psychological and mental health concepts change in meaning, salience, severity, and use across academic psychology, news media, books, and general language corpora. This stream includes substantive case studies of concepts such as trauma, mental illness, schizophrenia, introversion, and generic and emotion related mental health terminology. Together, these studies examine when concepts become more culturally prominent, more widely used, emotionally intense, or evaluatively charged, interpreted through psychological theory, concept creep research, and corpus evidence.

• Social meaning in contested language
  I also contribute to collaborative projects that use NLP and computational social science methods to study socially important language, including dehumanization of women in incel discourse, mental health stigma detection in online communication, identity/person-first language for mental health conditions, and lay understandings of the common good. These projects extend my broader interest in how language reflects, organizes, and reshapes social and psychological categories.