@inproceedings{mathias-bhattacharyya-2018-thank,
title = "Thank {``}Goodness{''}! A Way to Measure Style in Student Essays",
author = "Mathias, Sandeep and
Bhattacharyya, Pushpak",
editor = "Tseng, Yuen-Hsien and
Chen, Hsin-Hsi and
Ng, Vincent and
Komachi, Mamoru",
booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-3705",
doi = "10.18653/v1/W18-3705",
pages = "35--41",
abstract = "Essays have two major components for scoring - content and style. In this paper, we describe a property of the essay, called goodness, and use it to predict the score given for the style of student essays. We compare our approach to solve this problem with baseline approaches, like language modeling and also a state-of-the-art deep learning system. We show that, despite being quite intuitive, our approach is very powerful in predicting the style of the essays.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mathias-bhattacharyya-2018-thank">
<titleInfo>
<title>Thank “Goodness”! A Way to Measure Style in Student Essays</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sandeep</namePart>
<namePart type="family">Mathias</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pushpak</namePart>
<namePart type="family">Bhattacharyya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications</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">Vincent</namePart>
<namePart type="family">Ng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mamoru</namePart>
<namePart type="family">Komachi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Melbourne, Australia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Essays have two major components for scoring - content and style. In this paper, we describe a property of the essay, called goodness, and use it to predict the score given for the style of student essays. We compare our approach to solve this problem with baseline approaches, like language modeling and also a state-of-the-art deep learning system. We show that, despite being quite intuitive, our approach is very powerful in predicting the style of the essays.</abstract>
<identifier type="citekey">mathias-bhattacharyya-2018-thank</identifier>
<identifier type="doi">10.18653/v1/W18-3705</identifier>
<location>
<url>https://aclanthology.org/W18-3705</url>
</location>
<part>
<date>2018-07</date>
<extent unit="page">
<start>35</start>
<end>41</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Thank “Goodness”! A Way to Measure Style in Student Essays
%A Mathias, Sandeep
%A Bhattacharyya, Pushpak
%Y Tseng, Yuen-Hsien
%Y Chen, Hsin-Hsi
%Y Ng, Vincent
%Y Komachi, Mamoru
%S Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F mathias-bhattacharyya-2018-thank
%X Essays have two major components for scoring - content and style. In this paper, we describe a property of the essay, called goodness, and use it to predict the score given for the style of student essays. We compare our approach to solve this problem with baseline approaches, like language modeling and also a state-of-the-art deep learning system. We show that, despite being quite intuitive, our approach is very powerful in predicting the style of the essays.
%R 10.18653/v1/W18-3705
%U https://aclanthology.org/W18-3705
%U https://doi.org/10.18653/v1/W18-3705
%P 35-41
Markdown (Informal)
[Thank “Goodness”! A Way to Measure Style in Student Essays](https://aclanthology.org/W18-3705) (Mathias & Bhattacharyya, NLP-TEA 2018)
ACL
- Sandeep Mathias and Pushpak Bhattacharyya. 2018. Thank “Goodness”! A Way to Measure Style in Student Essays. In Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications, pages 35–41, Melbourne, Australia. Association for Computational Linguistics.