Morphological complexity of L2 discourse

Rurik Tywoniw, Scott Crossley

Research output: Chapter in Book/Report/Conference proceedingChapter


This chapter presents a study which merges two distinct topics in corpus linguistics: automated Natural Language Processing (NLP) and morphology complexity. NLP refers to using computational methods to automatically calculate the appearance of linguistic features in a text, typically in an efficient and large-scale fashion. The linguistic features range from type–token ratios and average word frequencies to the number of dependent clauses and the degree of semantic similarity across paragraphs in a text. Morphological features of language have historically been seen in linguistics as a gateway to understanding implicit knowledge about a language. Recent research in NLP has provided linguistic analysis innovations in the form of powerful and efficient automatic text analysis tools. These tools have been used to measure linguistic features which are too fine-grained or too numerous to feasibly count by hand.
Original languageEnglish (US)
Title of host publicationThe Routledge Handbook of Corpus Approaches to Discourse Analysis
EditorsEric Friginal, Jack A. Hardy
ISBN (Electronic)9780429259982
ISBN (Print)9780367201814
StatePublished - Dec 18 2020

Publication series

NameRoutledge Handbooks in Applied Linguistics


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