2027

QDLCA27 Pre-conference Workshop: Research Infrastructures, Methods, and Data for Quantitative Diachronic Linguistics

The QDLCA27 pre-conference workshop, organised by Andrea Farina, is a satellite event of the Quantitative Diachronic Linguistics and Cultural Analytics 2027 (QDLCA27) conference. The workshop will take place from Monday 11 (afternoon) to Wednesday 13 (morning) January 2027. It is supported by a CLARIN-funded initiative (Days 1–2) and by the COALA (Day 3), which also underpins the organisation of QDLCA27. Bringing together researchers interested in digital resources, computational methods, and linguistic annotation, the workshop is designed to provide participants with methodological knowledge and practical experience in the quantitative study of language change, while fostering collaboration across the historical linguistics, corpus linguistics, computational linguistics, and digital humanities communities.

Pre-conference workshop webpage here.

Quantitative Diachronic Linguistics and Cultural Analytics 2027 (QDLCA27)

We invite submissions for the second edition of the conference Quantitative Diachronic Linguistics and Cultural Analytics 2027 (QDLCA27), to be held at King’s College London (Strand Campus, WC2R 2LS, London) on 14–15 January 2027. This will be an in-person event.

Conference webpage here.

2026

Workshop: NLP tools and methods for diachronic lexical semantics

Together with Zinaida Geylikman, the COALA team (Andrea Farina, Barbara McGillivray, Bastien Liétard) will host this workshop as part of the conference Langues & Langage à la croisée des Disciplines (LLcD), Aix-Marseille Université, 14-16 December 2026.

The workshop aims to gather contributions on Natural Language Processing (NLP) tools and methods for the diachronic study of lexical semantics. The focus is on diachrony and lexical semantics, addressing two questions: (1) does the performance of NLP tools vary depending on the historical period and language considered? (2) when are manual or semi-automatic annotations more reliable than NLP methods?

Workshop webpage here.