Performance tags: who's running the show?

Emma Tonkin, G J L Tourte, A Zollers

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We describe a pilot study which specifically examines the prevalence and characteristics of performance tags on several sites. Identifying post-coordination of tags as a useful step in the study of this phenomenon, as well as other approaches to leveraging tags based on text and/or sentiment analysis, we demonstrate an approach to automation of this process, postcoordinating (segmenting) terms by means of a probabilistic model based around Markov chains. The effectiveness of this approach to parsing is evaluated with respect to the wide range of constructions visible on various services. Several candidate approaches for the latter stages of automated classification are identified.

Conference

ConferenceProceedings 19th Workshop of the American Society for Information Science and Technology Special Interest Group in Classification Research
CityColumbus, Ohio
Period1/01/08 → …

Fingerprint

Markov processes
Automation
Statistical Models

Cite this

Tonkin, E., Tourte, G. J. L., & Zollers, A. (2008). Performance tags: who's running the show? In Advances in Classification Research Online

Performance tags: who's running the show? / Tonkin, Emma; Tourte, G J L; Zollers, A.

Advances in Classification Research Online. 2008.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tonkin, E, Tourte, GJL & Zollers, A 2008, Performance tags: who's running the show? in Advances in Classification Research Online. Proceedings 19th Workshop of the American Society for Information Science and Technology Special Interest Group in Classification Research, Columbus, Ohio, 1/01/08.
Tonkin E, Tourte GJL, Zollers A. Performance tags: who's running the show? In Advances in Classification Research Online. 2008.
Tonkin, Emma ; Tourte, G J L ; Zollers, A. / Performance tags: who's running the show?. Advances in Classification Research Online. 2008.
@inproceedings{f79e932624d748cda238b96718daa1b2,
title = "Performance tags: who's running the show?",
abstract = "We describe a pilot study which specifically examines the prevalence and characteristics of performance tags on several sites. Identifying post-coordination of tags as a useful step in the study of this phenomenon, as well as other approaches to leveraging tags based on text and/or sentiment analysis, we demonstrate an approach to automation of this process, postcoordinating (segmenting) terms by means of a probabilistic model based around Markov chains. The effectiveness of this approach to parsing is evaluated with respect to the wide range of constructions visible on various services. Several candidate approaches for the latter stages of automated classification are identified.",
author = "Emma Tonkin and Tourte, {G J L} and A Zollers",
year = "2008",
language = "English",
booktitle = "Advances in Classification Research Online",

}

TY - GEN

T1 - Performance tags: who's running the show?

AU - Tonkin,Emma

AU - Tourte,G J L

AU - Zollers,A

PY - 2008

Y1 - 2008

N2 - We describe a pilot study which specifically examines the prevalence and characteristics of performance tags on several sites. Identifying post-coordination of tags as a useful step in the study of this phenomenon, as well as other approaches to leveraging tags based on text and/or sentiment analysis, we demonstrate an approach to automation of this process, postcoordinating (segmenting) terms by means of a probabilistic model based around Markov chains. The effectiveness of this approach to parsing is evaluated with respect to the wide range of constructions visible on various services. Several candidate approaches for the latter stages of automated classification are identified.

AB - We describe a pilot study which specifically examines the prevalence and characteristics of performance tags on several sites. Identifying post-coordination of tags as a useful step in the study of this phenomenon, as well as other approaches to leveraging tags based on text and/or sentiment analysis, we demonstrate an approach to automation of this process, postcoordinating (segmenting) terms by means of a probabilistic model based around Markov chains. The effectiveness of this approach to parsing is evaluated with respect to the wide range of constructions visible on various services. Several candidate approaches for the latter stages of automated classification are identified.

UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-84869818144&partnerID=40&md5=055234755f978a6ea5abd888b1c5b0a6

M3 - Conference contribution

BT - Advances in Classification Research Online

ER -