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Article

Towards genre classification for IR in the workplace

Authors:

Charles L. A. Clarke,

Elaine G. TomsAuthors Info & Claims

IIiX: Proceedings of the 1st international conference on Information interaction in context

Pages 30 - 36

https://doi.org/10.1145/1164820.1164829

Published: 18 October 2006 Publication History

Abstract

Use of document genre in information retrieval systems has the potential to improve the task-appropriateness of results. However, genre classification remains a challenging problem. We describe a case study of genre classification in a software engineering workplace domain, which includes the development of a genre taxonomy and experiments in automatic genre classification using supervised machine learning. We present results based on evaluation using real-life enterprise data from this work domain.

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Cited By

Dodson S(2024)Using genres for laboratory work, report writing and design projects: comparing second and fourth year undergraduate engineersInformation and Learning Sciences10.1108/ILS-03-2024-0031Online publication date: 17-Sep-2024
https://doi.org/10.1108/ILS-03-2024-0031
Dodson SSinnamon LKopak R(2024)Mapping the relationship between genres and tasks: A study of undergraduate engineersJournal of the Association for Information Science and Technology10.1002/asi.24897Online publication date: 20-Apr-2024
https://doi.org/10.1002/asi.24897
Kuzman TLjubešić N(2023)Automatic genre identification: a surveyLanguage Resources and Evaluation10.1007/s10579-023-09695-8Online publication date: 16-Nov-2023
https://doi.org/10.1007/s10579-023-09695-8
Show More Cited By

Index Terms

Towards genre classification for IR in the workplace
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Other conferences

IIiX: Proceedings of the 1st international conference on Information interaction in context

October 2006

187 pages

ISBN:1595934820

DOI:10.1145/1164820

Program Chair:
Ian Ruthven
University of Strathclyde, UK

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 October 2006

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Cited By

Dodson S(2024)Using genres for laboratory work, report writing and design projects: comparing second and fourth year undergraduate engineersInformation and Learning Sciences10.1108/ILS-03-2024-0031Online publication date: 17-Sep-2024
https://doi.org/10.1108/ILS-03-2024-0031
Dodson SSinnamon LKopak R(2024)Mapping the relationship between genres and tasks: A study of undergraduate engineersJournal of the Association for Information Science and Technology10.1002/asi.24897Online publication date: 20-Apr-2024
https://doi.org/10.1002/asi.24897
Kuzman TLjubešić N(2023)Automatic genre identification: a surveyLanguage Resources and Evaluation10.1007/s10579-023-09695-8Online publication date: 16-Nov-2023
https://doi.org/10.1007/s10579-023-09695-8
Dodson S(2023)Tracing Information Use Over Time: A Comparative Study of Undergraduate EngineersProceedings of the Association for Information Science and Technology10.1002/pra2.90460:1(938-940)Online publication date: 22-Oct-2023
https://doi.org/10.1002/pra2.904
Degaetano-Ortlieb SSäily TBizzoni Y(2021)Registerial Adaptation vs. Innovation Across Situational Contexts: 18th Century Women in TransitionFrontiers in Artificial Intelligence10.3389/frai.2021.6099704Online publication date: 1-Jun-2021
https://doi.org/10.3389/frai.2021.609970
Nguyen HRohrbaugh G(2019)Cross-lingual genre classification using linguistic groupingsJournal of Computing Sciences in Colleges10.5555/3306465.330647934:3(91-96)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.5555/3306465.3306479
Argamon S(2019)Register in computational language researchRegister Studies10.1075/rs.18015.arg1:1(100-135)Online publication date: 26-Apr-2019
https://doi.org/10.1075/rs.18015.arg
Stede MSchneider J(2018)Argumentation MiningSynthesis Lectures on Human Language Technologies10.2200/S00883ED1V01Y201811HLT04011:2(1-191)Online publication date: 20-Dec-2018
https://doi.org/10.2200/S00883ED1V01Y201811HLT040
Davoodi EKosseim LBachand FLaali MArgollo E(2018)Classification of Textual Genres Using Discourse InformationComputational Linguistics and Intelligent Text Processing10.1007/978-3-319-75477-2_46(636-647)Online publication date: 21-Mar-2018
https://doi.org/10.1007/978-3-319-75477-2_46
KRÜGER KLUKOWIAK ASONNTAG JWARZECHA SSTEDE M(2017)Classifying news versus opinions in newspapers: Linguistic features for domain independenceNatural Language Engineering10.1017/S135132491700004323:05(687-707)Online publication date: 21-Feb-2017
https://doi.org/10.1017/S1351324917000043
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