@inproceedings{horbach-etal-2017-influence,
title = "The Influence of Spelling Errors on Content Scoring Performance",
author = "Horbach, Andrea and
Ding, Yuning and
Zesch, Torsten",
editor = "Tseng, Yuen-Hsien and
Chen, Hsin-Hsi and
Lee, Lung-Hao and
Yu, Liang-Chih",
booktitle = "Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA} 2017)",
month = dec,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/W17-5908",
pages = "45--53",
abstract = "Spelling errors occur frequently in educational settings, but their influence on automatic scoring is largely unknown. We therefore investigate the influence of spelling errors on content scoring performance using the example of the ASAP corpus. We conduct an annotation study on the nature of spelling errors in the ASAP dataset and utilize these finding in machine learning experiments that measure the influence of spelling errors on automatic content scoring. Our main finding is that scoring methods using both token and character n-gram features are robust against spelling errors up to the error frequency in ASAP.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="horbach-etal-2017-influence">
<titleInfo>
<title>The Influence of Spelling Errors on Content Scoring Performance</title>
</titleInfo>
<name type="personal">
<namePart type="given">Andrea</namePart>
<namePart type="family">Horbach</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuning</namePart>
<namePart type="family">Ding</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Torsten</namePart>
<namePart type="family">Zesch</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yuen-Hsien</namePart>
<namePart type="family">Tseng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hsin-Hsi</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lung-Hao</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Liang-Chih</namePart>
<namePart type="family">Yu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Asian Federation of Natural Language Processing</publisher>
<place>
<placeTerm type="text">Taipei, Taiwan</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Spelling errors occur frequently in educational settings, but their influence on automatic scoring is largely unknown. We therefore investigate the influence of spelling errors on content scoring performance using the example of the ASAP corpus. We conduct an annotation study on the nature of spelling errors in the ASAP dataset and utilize these finding in machine learning experiments that measure the influence of spelling errors on automatic content scoring. Our main finding is that scoring methods using both token and character n-gram features are robust against spelling errors up to the error frequency in ASAP.</abstract>
<identifier type="citekey">horbach-etal-2017-influence</identifier>
<location>
<url>https://aclanthology.org/W17-5908</url>
</location>
<part>
<date>2017-12</date>
<extent unit="page">
<start>45</start>
<end>53</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T The Influence of Spelling Errors on Content Scoring Performance
%A Horbach, Andrea
%A Ding, Yuning
%A Zesch, Torsten
%Y Tseng, Yuen-Hsien
%Y Chen, Hsin-Hsi
%Y Lee, Lung-Hao
%Y Yu, Liang-Chih
%S Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017)
%D 2017
%8 December
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F horbach-etal-2017-influence
%X Spelling errors occur frequently in educational settings, but their influence on automatic scoring is largely unknown. We therefore investigate the influence of spelling errors on content scoring performance using the example of the ASAP corpus. We conduct an annotation study on the nature of spelling errors in the ASAP dataset and utilize these finding in machine learning experiments that measure the influence of spelling errors on automatic content scoring. Our main finding is that scoring methods using both token and character n-gram features are robust against spelling errors up to the error frequency in ASAP.
%U https://aclanthology.org/W17-5908
%P 45-53
Markdown (Informal)
[The Influence of Spelling Errors on Content Scoring Performance](https://aclanthology.org/W17-5908) (Horbach et al., NLP-TEA 2017)
ACL
- Andrea Horbach, Yuning Ding, and Torsten Zesch. 2017. The Influence of Spelling Errors on Content Scoring Performance. In Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017), pages 45–53, Taipei, Taiwan. Asian Federation of Natural Language Processing.