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Statistical sequence and parsing models for descriptive linguistics and psycholinguistics
Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.ORCID iD: 0000-0003-2815-395X
2016 (English)In: New Approaches to English Linguistics: Building bridges / [ed] Olga Timofeeva, Anne-Christine Gardner, Alpo Honkapohja, Sarah Chevalier, John Benjamins Publishing Company, 2016, 281-320 p.Chapter in book (Refereed)
Abstract [en]

This study shows that using computational linguistic models is beneficial for descriptive linguistics and psycholinguistics. It applies two models to various English genres and learner language: 1) surprisal and 2) a syntactic parser, allowing us to investigate the role of ambiguity and the interplay between idiom and syntax principles. We find that surprisal and ambiguity are higher for learner language, while parser scores and model fit are lower. In addition, the random application of alternations leads to more ambiguous sentences. Failures to generate optimal orderings in the sense of relevance theory, such as nonnative-like utterances by language learners exhibit, increase processing load, both for human and automatic processors. As human and automatic parsing difficulties correlate, we suggest syntactic parsers as psycholinguistic processing models.

Place, publisher, year, edition, pages
John Benjamins Publishing Company, 2016. 281-320 p.
, Studies in Language Companion Series, ISSN 0165-7763 ; 177
Keyword [en]
syntactic parsing, ambiguity, idiom and syntax principle, statistical models, language processing
National Category
General Language Studies and Linguistics
Research subject
URN: urn:nbn:se:su:diva-134664ISBN: 9789027259424OAI: diva2:1037150
Available from: 2016-10-14 Created: 2016-10-14 Last updated: 2016-10-14Bibliographically approved

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Grigonyte, Gintare
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