Towards genre classification for IR in the workplace

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.

References

1. Hawking, D.: Challenges in Enterprise Search, presented at the Australasian Database Conference, Dunedin, New Zealand (2004)
2. Fagin, R. Kumar, K. S. McCurley, J. Novak, D. Sivakumar, Tomlin, J. A., Williamson D. P.: Searching the Workplace Web. presented at WWW '03 International World Web Conference (2003)
3. Broder, A., Ciccolo, A. C.: Towards the Next Generation of Enterprise Search Technology. IBM Systems Journal 43 (2004) 451--454
4. Jarvelin, K., Ingwersen, P.: Information Seeking Research Needs Extensions towards Tasks and Technology. Information Research 10 (2004)
5. Anderson, C. J., Glassman, M, McAfee R. B., Pinelli, T.: An Investigation of Factors Afffecting how Engineers and Scientists Seek Information. Journal of Engineering and Technology Management 18 (2001) 131--155
6. Hertzum, M.: The Importance of Trust in Software Engineers' Assessment and Choice of Information Sources. Information and Organization 12 (2002) 1--18
7. Fidel, R., Green, M.: The Many Faces of Accessibility: Engineers' Perception of Information Sources. Information Processing & Management 40 (2004) 563--581
8. Freund, L., Toms, E. G., Clarke, C. L. A.: Modeling Task-Genre Relationships for IR in the Workplace. Annual International ACM SIGIR Conference, Salvador, Brazil (2005)
9. Freund, L., Toms, E. G., Waterhouse, J.: Modeling the Information Behaviour of Software Engineers using a Work -Task Framework. 68th Annual Meeting of the American Society for Information Science and Technology, Charlotte, NC (2005).
10. Vakkari, P.: Task-Based Information Searching. Annual Review of Information Science and Technology 37 (2003) 413--463
11. Bystrom, K., Hansen, P.: Conceptual Framework for Tasks in Information Studies. Journal of the American Society for Information Science and Technology 56 (2005) 1050--1061
12. Bystrom, K.: Information and Information Sources in Tasks of Varying Complexity. Journal of the American Society for Information Science 53 (2002) 581--591
13. Orlikowski, W. J., Yates, J.: Genre Repertoire: the Structuring of Communicative Practices in Organizations. Administrative Science Quarterly 39 (1994) 541--574
14. Toms, E. G.: Recognizing Digital Genre. Bulletin of the American Society for information Science and Technology 27 (2001) 20--22
15. Yoshioka, T., Herman, G., Yates, J., Orlikowski, W. J.: Genre Taxonomy: a Knowledge Repository of Communicative Actions. ACM Transactions on Information Systems 19 (2001) 431--456
16. Karlgren, J.: Non-Topical Factors in Information Access. Webnet '99, Honolulu, 1999.
17. Roussinov, D. G., Crowston, K., Nilan, M., Kwasnik, B., Cai, J., Liu, X.: Genre Based Navigation on the Web. presented at Hawai'i International Conference on Systems Sciences, Maui, Hawai'i (2001)
18. Muresan, G., Smith, C. L., Cole, M. Liu, L. Belkin, N. J.: Detecting Document Genre for Personalization in Information Retrieval. Proceedings of the Hawaii International Conference on System Sciences, Kauai, Hawai'I (2006)
19. Bretan, I., Dewe, J., Hallberg, A., Wolkert, N., Karlgren, J.: Web-Specific Genre Visualization. presented at WebNet '98, Orlando Florida (1998)
20. Glover, E. J., Lawrence, S., Gordon, M. D., Birmingham, W. P., Giles, C. L.: Web Search -- Your Way. Communications of the ACM 44 (2001) 97--102
21. Dewdney, N., VanEss-Dykema, C., MacMillan, R.: The Form is the Substance: Classification of Genres in Text. Proceedings of ACL Workshop on Human Language Technology and Knowledge Management (2001)
22. Meyer zu Eissen, S., Stein, B.: Genre Classification of Web Pages. Proceedings of the 27th German Conference on Artificial Intelligence, Ulm, Germany (2004)
23. Lee, Y.-B., Myaeng, S. H.: Text Genre Classification with Genre Revealing and Subject-Revealing Features. Proceedings of the 25th Annual International ACM SIGIR Conference (2002)
24. Stamatatos, E., Fakotakis, N., Kokkinakis, G.: Text Genre Detection using Common Word Frequencies. Proceedings of the 18th International Conference on Computational Linguistics (2000)
25. Finn, A., Kushmerick, N.: Learning to Classify Documents according to Genre. presented at IJCAI Workshop on Computational Approaches to Style Analysis and Synthesis (2003)
26. Shepherd, M., Watters, C., Kennedy, A.: Cybergenre: Automatic Identification of Home Pages on the Web. Journal of Web Engineering 3 (2004) 236--251
27. Joachims, T.: Learning to Classify Text using Support Vector Machines: Methods, Theory and Algorithms, Kluwer, Amsterdam (2002)
28. Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. Proceedings of the European Conference on Machine Learning (1998)

Index Terms

• Information systems

  • Information retrieval