DE eng

Search in the Catalogues and Directories

Hits 1 – 15 of 15

1
Using linguistic features to predict the response process complexity associated with answering clinical MCQs
Baldwin, Peter; Ha, Le An; Yaneva, Victoria. - : Association for Computational Linguistics, 2021
BASE
Show details
2
“Keep it simple!”: an eye-tracking study for exploring complexity and distinguishability of web pages for people with autism [<Journal>]
Eraslan, Sukru [Verfasser]; Yesilada, Yeliz [Verfasser]; Yaneva, Victoria [Verfasser].
DNB Subject Category Language
Show details
3
Automated Prediction of Examinee Proficiency from Short-Answer Questions ...
BASE
Show details
4
Contributions to the Computational Treatment of Non-literal Language
Rohanian, Omid. - : University of Wolverhampton, 2020
BASE
Show details
5
Using natural language processing to predict item response times and improve test construction
In: 2020 ; 1 ; 27 (2020)
BASE
Show details
6
Automated prediction of examinee proficiency from short-answer questions
Clauser, Brian; Morales, Amy; Pandian, Ravi. - : International Committee on Computational Linguistics, 2020
BASE
Show details
7
Predicting the difficulty of multiple choice questions in a high-stakes medical exam
Yaneva, Victoria; Mee, Janet; Ha, Le. - : Association for Computational Linguistics, 2019
BASE
Show details
8
Classifying referential and non-referential it using gaze
In: 4896 ; 4901 (2018)
Abstract: When processing a text, humans and machines must disambiguate between different uses of the pronoun it, including non-referential, nominal anaphoric or clause anaphoric ones. In this paper, we use eye-tracking data to learn how humans perform this disambiguation. We use this knowledge to improve the automatic classification of it. We show that by using gaze data and a POS-tagger we are able to significantly outperform a common baseline and classify between three categories of it with an accuracy comparable to that of linguisticbased approaches. In addition, the discriminatory power of specific gaze features informs the way humans process the pronoun, which, to the best of our knowledge, has not been explored using data from a natural reading task.
Keyword: eye-tracking; gaze data; machine learning; non-referential it; pleonastic it; pronoun classification
URL: http://hdl.handle.net/2436/622195
https://doi.org/10.18653/v1/D18-1528
BASE
Hide details
9
Using gaze data to predict multiword expressions
BASE
Show details
10
Combining Multiple Corpora for Readability Assessment for People with Cognitive Disabilities
Yaneva, Victoria; Orăsan, Constantin; Evans, Richard. - : Association for Computational Linguistics, 2017
BASE
Show details
11
Cognitive processing of multiword expressions in native and non-native speakers of English: evidence from gaze data
In: 10596 ; 363 ; 379 (2017)
BASE
Show details
12
Effects of lexical properties on viewing time per word in autistic and neurotypical readers
In: 69 ; 1 ; 158 ; 167 (2017)
BASE
Show details
13
Assessing text and web accessibility for people with autism spectrum disorder
BASE
Show details
14
Accessible texts for autism: an eye-tracking study
Yaneva, Victoria; Temnikova, Irina; Mitkov, Ruslan Prof.. - : Association of Computing Machinery, 2016
BASE
Show details
15
Six good predictors of autistic reading comprehension
Yaneva, Victoria; Evans, Richard. - : INCOMA Ltd, 2015
BASE
Show details

Catalogues
0
0
0
0
1
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
14
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern