Header

UZH-Logo

Maintenance Infos

Statistical sequence and parsing models for descriptive linguistics and psycholinguistics


Schneider, G; Grigonyte, Gintare (2016). Statistical sequence and parsing models for descriptive linguistics and psycholinguistics. In: Timofeeva, Olga; Chevalier, Sarah; Gardner, Anne-Christine; Honkapohja, Alpo. New Approaches in English Linguistics : Building Bridges. Amsterdam: John Benjamins Publishing, 281-320.

Abstract

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.

Abstract

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.

Statistics

Altmetrics

Downloads

2 downloads since deposited on 21 Feb 2017
2 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:06 Faculty of Arts > English Department
06 Faculty of Arts > Institute of Computational Linguistics
06 Faculty of Arts > Center for Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Uncontrolled Keywords:language processing, statistical models, idiom and syntax principle, ambiguity, syntactic parsing
Language:English
Date:November 2016
Deposited On:21 Feb 2017 13:39
Last Modified:08 Dec 2017 23:41
Publisher:John Benjamins Publishing
ISBN:9789027259424
Publisher DOI:https://doi.org/10.1075/slcs.177
Related URLs:https://benjamins.com/#catalog/books/slcs.177/main (Publisher)

Download