Words like trauma, mental illness, and schizophrenia do not stay still. Concept creep describes one way these shifts can happen: harm-related concepts have expanded in scope and softened in severity since the late twentieth century, coming to describe a broader and milder range of experiences than they once did. These changes matter for how people understand, label, and seek help for distress.
I build computational tools to measure this kind of change precisely — and to study semantic change more broadly. My frameworks, SIBling and LSC-Eval, are designed to detect and evaluate specific kinds of semantic change across domains: whether a word is broadening in scope, gaining emotional intensity, or shifting in evaluative tone. My PhD applies these tools to mental health concepts, tracing how terms like schizophrenia have changed their meaning across decades of historical text - and what those shifts reveal about how psychological categories are culturally constructed and contested. The methods are computational, but the questions are substantive.
Most recently, I collaborated with Change is Key! (Riksbankens Jubileumsfond) to develop SenseRel, a benchmark for evaluating how well language models capture meaning relations at the sense level. I am a PhD researcher at the University of Melbourne working at the intersection of natural language processing and social psychology, supported by the Australian Government Research Training Program Scholarship and ARC-funded Concept Creep research funding.
PhD, Social Psychology & Natural Language Processing
University of Melbourne (2023-26)
Graduate Diploma in Psychology (Advanced) with Honours
University of Melbourne
ACL 2026: Joint-first author on SenseRel, a sense-level benchmark for denotational and connotational meaning relations, developed through my Change is Key! research internship.
Findings of ACL 2025: Lead author of LSC-Eval, a framework for evaluating methods for detecting dimensions of lexical semantic change using LLM-generated synthetic data.
ACL 2024: Lead author of SIBling, a multidimensional framework for modeling lexical semantic change with social science applications.
ICWSM 2026: Lead author of a computational social science paper on dehumanization of women and men in incel discourse.
Awards and research support: Australian Government Research Training Program Scholarship; selected for Change is Key! research support for an international research internship and collaboration; supported by Concept Creep research funding for conference travel and research dissemination.
Competitive team funding: Co-recipient of AUD $15,000 Hallmark Research Initiative seed funding for a 7-member team project on ‘Automatic evaluation of mental health stigma in online communication’, led by Yulia Otmakhova.
Research leadership and service: Australian English Language Co-Lead with Christine de Kock for the BLEnD SemEval-2026 Shared Task; Program Chair/PC for the LChange'26 Workshop, co-located with EACL 2026.
ACL Best Resource Paper Award: Contributor to BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages, awarded Best Resource Paper at ACL 2025; contributed Romanian-language gold-standard annotation (team lead: Daniela Teodorescu).
Invited talks: Presented PhD work on modeling semantic change in mental health concepts at international research groups: Utrecht University, the University of Gothenburg, and the National Research Council Canada.
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.
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:
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.

Much of my work begins with a measurement problem: existing methods for detecting semantic change often conflate distinct kinds of shift — broadening is not the same as softening or acquiring negative associations, yet most approaches treat these as one phenomenon. I believe getting measurement to approximate the construct as closely as possible is not a technical detail but a substantive one; imprecise tools produce imprecise claims about how language and culture change. SIBling, LSC-Eval, and SenseRel all emerged from this concern — frameworks designed to make semantic change research more interpretable, more evaluable, and more honest about what is and isn’t being measured.