@inproceedings{ozaku-etal-2006-features,
title = "Features of Terms in Actual Nursing Activities",
author = "Ozaku, Hiromi itoh and
Abe, Akinori and
Sagara, Kaoru and
Kuwahara, Noriaki and
Kogure, Kiyoshi",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Gangemi, Aldo and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Tapias, Daniel",
booktitle = "Proceedings of the Fifth International Conference on Language Resources and Evaluation ({LREC}{'}06)",
month = may,
year = "2006",
address = "Genoa, Italy",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2006/pdf/58_pdf.pdf",
abstract = "In this paper, we analyze nurses' dialogue and conversation data sets after manual transcriptions and show their features. Recently, medical risk management has been recognized as very important for both hospitals and their patients. To carry out medical risk management, it is important to model nursing activities as well as to collect many accident and incident examples. Therefore, we are now researching strategies of modeling nursing activities in order to understand them (E-nightingale Project). To model nursing activities, it is necessary to collect data of nurses' activities in actual situations and to accurately understand these activities and situations. We developed a method to determine any type of nursing activity from voice data. However we found that our method could not determine several activities because it misunderstood special nursing terms. To improve the accuracy of this method, we focus on analyzing nurses' dialogue and conversation data and on collecting special nursing terms. We have already collected 800 hours of nurses' dialogue and conversation data sets in hospitals to find the tendencies and features of how nurses use special terms such as abbreviations and jargon as well as new terms. Consequently, in this paper we categorize nursing terms according to their usage and effectiveness. In addition, based on the results, we show a rough strategy for building nursing dictionaries.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ozaku-etal-2006-features">
<titleInfo>
<title>Features of Terms in Actual Nursing Activities</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hiromi</namePart>
<namePart type="given">itoh</namePart>
<namePart type="family">Ozaku</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Akinori</namePart>
<namePart type="family">Abe</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kaoru</namePart>
<namePart type="family">Sagara</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Noriaki</namePart>
<namePart type="family">Kuwahara</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kiyoshi</namePart>
<namePart type="family">Kogure</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2006-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nicoletta</namePart>
<namePart type="family">Calzolari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Khalid</namePart>
<namePart type="family">Choukri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aldo</namePart>
<namePart type="family">Gangemi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bente</namePart>
<namePart type="family">Maegaard</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joseph</namePart>
<namePart type="family">Mariani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jan</namePart>
<namePart type="family">Odijk</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniel</namePart>
<namePart type="family">Tapias</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>European Language Resources Association (ELRA)</publisher>
<place>
<placeTerm type="text">Genoa, Italy</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this paper, we analyze nurses’ dialogue and conversation data sets after manual transcriptions and show their features. Recently, medical risk management has been recognized as very important for both hospitals and their patients. To carry out medical risk management, it is important to model nursing activities as well as to collect many accident and incident examples. Therefore, we are now researching strategies of modeling nursing activities in order to understand them (E-nightingale Project). To model nursing activities, it is necessary to collect data of nurses’ activities in actual situations and to accurately understand these activities and situations. We developed a method to determine any type of nursing activity from voice data. However we found that our method could not determine several activities because it misunderstood special nursing terms. To improve the accuracy of this method, we focus on analyzing nurses’ dialogue and conversation data and on collecting special nursing terms. We have already collected 800 hours of nurses’ dialogue and conversation data sets in hospitals to find the tendencies and features of how nurses use special terms such as abbreviations and jargon as well as new terms. Consequently, in this paper we categorize nursing terms according to their usage and effectiveness. In addition, based on the results, we show a rough strategy for building nursing dictionaries.</abstract>
<identifier type="citekey">ozaku-etal-2006-features</identifier>
<location>
<url>http://www.lrec-conf.org/proceedings/lrec2006/pdf/58_pdf.pdf</url>
</location>
<part>
<date>2006-05</date>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Features of Terms in Actual Nursing Activities
%A Ozaku, Hiromi itoh
%A Abe, Akinori
%A Sagara, Kaoru
%A Kuwahara, Noriaki
%A Kogure, Kiyoshi
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Gangemi, Aldo
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Tapias, Daniel
%S Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
%D 2006
%8 May
%I European Language Resources Association (ELRA)
%C Genoa, Italy
%F ozaku-etal-2006-features
%X In this paper, we analyze nurses’ dialogue and conversation data sets after manual transcriptions and show their features. Recently, medical risk management has been recognized as very important for both hospitals and their patients. To carry out medical risk management, it is important to model nursing activities as well as to collect many accident and incident examples. Therefore, we are now researching strategies of modeling nursing activities in order to understand them (E-nightingale Project). To model nursing activities, it is necessary to collect data of nurses’ activities in actual situations and to accurately understand these activities and situations. We developed a method to determine any type of nursing activity from voice data. However we found that our method could not determine several activities because it misunderstood special nursing terms. To improve the accuracy of this method, we focus on analyzing nurses’ dialogue and conversation data and on collecting special nursing terms. We have already collected 800 hours of nurses’ dialogue and conversation data sets in hospitals to find the tendencies and features of how nurses use special terms such as abbreviations and jargon as well as new terms. Consequently, in this paper we categorize nursing terms according to their usage and effectiveness. In addition, based on the results, we show a rough strategy for building nursing dictionaries.
%U http://www.lrec-conf.org/proceedings/lrec2006/pdf/58_pdf.pdf
Markdown (Informal)
[Features of Terms in Actual Nursing Activities](http://www.lrec-conf.org/proceedings/lrec2006/pdf/58_pdf.pdf) (Ozaku et al., LREC 2006)
ACL
- Hiromi itoh Ozaku, Akinori Abe, Kaoru Sagara, Noriaki Kuwahara, and Kiyoshi Kogure. 2006. Features of Terms in Actual Nursing Activities. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA).