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PRODID:-//pretalx//conference-hub.linguistic-society.com//athens-2026//talk
 //SR3MTB
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UID:pretalx-athens-2026-SR3MTB@conference-hub.linguistic-society.com
DTSTART:20260424T120000Z
DTEND:20260424T123000Z
DESCRIPTION:Outline\nMulti-word expressions (MWEs) such as of course\, in t
 he light of\, as good as new or take into account are\ncentral to fluent c
 ommunication\, yet relatively difficult to identify systematically. This l
 ecture draws on the\nforthcoming Frequency Dictionary of Multi-Word Expres
 sions in British English (Brezina & Gablasova\,\nRoutledge\, 2026) to pres
 ent a clear\, corpus-based method for analysing a wide range of MWEs acros
 s genres.\nI begin by contrasting corpus evidence with current AI language
  models. Modern AI generates language by\nanswering a simple question – 
 What is the next word? – a principle long used in corpus linguistics to 
 measure\ncollocation. Yet while AI can imitate fluent usage\, corpora rema
 in more transparent and reliable for identifying\nMWEs because they trace 
 patterns directly to authentic human interaction in speech and writing.\nT
 he lecture outlines a practical framework combining frequency\, associatio
 n strength and dispersion to\ncapture the core phraseology of contemporary
  British English. Examples from the dictionary illustrate how this\nmethod
  reveals stable\, meaningful MWEs and supports applications in language te
 aching\, language testing\,\nlexicography and applied linguistic research.
  The central claim is this: AI can model language\, but corpora allow\nus 
 to understand it.
DTSTAMP:20260419T082253Z
LOCATION:Online Session
SUMMARY:How to Identify Multi-Word Expressions in Corpora? - Vaclav Brezina
URL:https://conference-hub.linguistic-society.com/athens-2026/talk/SR3MTB/
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