Identifying interpersonal distance using systemic features

Maria Herke-Couchman; Casey Whitelaw; Jon Patrick

Identifying interpersonal distance using systemic features

Maria Herke-Couchman^*, Casey Whitelaw, Jon Patrick

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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Abstract

This paper uses Systemic Functional Linguistic (SFL) theory as a basis for extracting semantic features of documents. We focus on the pronominal and determination system and the role it plays in constructing interpersonal distance. By using a hierarchical system model that represents the author’s language choices, it is possible to construct a rich and informative feature representation. Using these systemic features, we report clear separation between registers with different interpersonal distance.

Original language	English
Title of host publication	Exploring Attitude and Affect in Text: Theories and Applications
Subtitle of host publication	Papers from the AAAI Spring Symposium
Editors	Yan Qu, James Shanhan, Janyce Wiebe
Place of Publication	California,USA
Publisher	Association for the Advancement of Artificial Intelligence
Pages	71-74
Number of pages	4
Volume	SS-04-07
ISBN (Print)	157735219X
Publication status	Published - 2004
Externally published	Yes
Event	2004 AAAI Spring Symposium - Stanford, CA, United States Duration: 22 Mar 2004 → 24 Mar 2004

Other

Other	2004 AAAI Spring Symposium
Country/Territory	United States
City	Stanford, CA
Period	22/03/04 → 24/03/04

Bibliographical note

Copyright the Publisher. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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Publisher version (open access)Final published version, 40.3 KBLicence: Unspecified

http://www.aaai.org/Papers/Symposia/Spring/2004/SS-04-07/SS04-07-013.pdf

Cite this

Herke-Couchman, M., Whitelaw, C., & Patrick, J. (2004). Identifying interpersonal distance using systemic features. In Y. Qu, J. Shanhan, & J. Wiebe (Eds.), Exploring Attitude and Affect in Text: Theories and Applications: Papers from the AAAI Spring Symposium (Vol. SS-04-07, pp. 71-74). Association for the Advancement of Artificial Intelligence. http://www.aaai.org/Papers/Symposia/Spring/2004/SS-04-07/SS04-07-013.pdf

Herke-Couchman, Maria ; Whitelaw, Casey ; Patrick, Jon. / Identifying interpersonal distance using systemic features. Exploring Attitude and Affect in Text: Theories and Applications: Papers from the AAAI Spring Symposium. editor / Yan Qu ; James Shanhan ; Janyce Wiebe. Vol. SS-04-07 California,USA : Association for the Advancement of Artificial Intelligence, 2004. pp. 71-74

@inproceedings{fbb3747a974b45219987ebafc1fd2d5e,

title = "Identifying interpersonal distance using systemic features",

abstract = "This paper uses Systemic Functional Linguistic (SFL) theory as a basis for extracting semantic features of documents. We focus on the pronominal and determination system and the role it plays in constructing interpersonal distance. By using a hierarchical system model that represents the author{\textquoteright}s language choices, it is possible to construct a rich and informative feature representation. Using these systemic features, we report clear separation between registers with different interpersonal distance.",

author = "Maria Herke-Couchman and Casey Whitelaw and Jon Patrick",

note = "Copyright the Publisher. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.; 2004 AAAI Spring Symposium ; Conference date: 22-03-2004 Through 24-03-2004",

year = "2004",

language = "English",

isbn = "157735219X",

volume = "SS-04-07",

pages = "71--74",

editor = "Yan Qu and James Shanhan and Janyce Wiebe",

booktitle = "Exploring Attitude and Affect in Text: Theories and Applications",

publisher = "Association for the Advancement of Artificial Intelligence",

address = "United States",

}

Herke-Couchman, M, Whitelaw, C & Patrick, J 2004, Identifying interpersonal distance using systemic features. in Y Qu, J Shanhan & J Wiebe (eds), Exploring Attitude and Affect in Text: Theories and Applications: Papers from the AAAI Spring Symposium. vol. SS-04-07, Association for the Advancement of Artificial Intelligence, California,USA, pp. 71-74, 2004 AAAI Spring Symposium, Stanford, CA, United States, 22/03/04. <http://www.aaai.org/Papers/Symposia/Spring/2004/SS-04-07/SS04-07-013.pdf>

Identifying interpersonal distance using systemic features. / Herke-Couchman, Maria; Whitelaw, Casey; Patrick, Jon.
Exploring Attitude and Affect in Text: Theories and Applications: Papers from the AAAI Spring Symposium. ed. / Yan Qu; James Shanhan; Janyce Wiebe. Vol. SS-04-07 California,USA: Association for the Advancement of Artificial Intelligence, 2004. p. 71-74.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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T1 - Identifying interpersonal distance using systemic features

AU - Herke-Couchman, Maria

AU - Whitelaw, Casey

AU - Patrick, Jon

N1 - Copyright the Publisher. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2004

Y1 - 2004

N2 - This paper uses Systemic Functional Linguistic (SFL) theory as a basis for extracting semantic features of documents. We focus on the pronominal and determination system and the role it plays in constructing interpersonal distance. By using a hierarchical system model that represents the author’s language choices, it is possible to construct a rich and informative feature representation. Using these systemic features, we report clear separation between registers with different interpersonal distance.

AB - This paper uses Systemic Functional Linguistic (SFL) theory as a basis for extracting semantic features of documents. We focus on the pronominal and determination system and the role it plays in constructing interpersonal distance. By using a hierarchical system model that represents the author’s language choices, it is possible to construct a rich and informative feature representation. Using these systemic features, we report clear separation between registers with different interpersonal distance.

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UR - http://www.researchonline.mq.edu.au/vital/access/manager/Repository/mq:15645

M3 - Conference proceeding contribution

AN - SCOPUS:32944469456

SN - 157735219X

VL - SS-04-07

SP - 71

EP - 74

BT - Exploring Attitude and Affect in Text: Theories and Applications

A2 - Qu, Yan

A2 - Shanhan, James

A2 - Wiebe, Janyce

PB - Association for the Advancement of Artificial Intelligence

CY - California,USA

T2 - 2004 AAAI Spring Symposium

Y2 - 22 March 2004 through 24 March 2004

ER -

Identifying interpersonal distance using systemic features

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