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Combining linguistic and machine learning techniques for email summarization
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Source Annual Meeting of the ACL archive
Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7 table of contents
Toulouse, France
Article No. 19  
Year of Publication: 2001
Authors
Smaranda Muresan  Columbia University, New York, NY
Evelyne Tzoukermann  Lucent Technologies, Murray Hill, NJ
Judith L. Klavans  Columbia University, New York, NY
Publisher
Association for Computational Linguistics  Morristown, NJ, USA
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Downloads (6 Weeks): 6,   Downloads (12 Months): 49,   Citation Count: 8
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DOI Bookmark: 10.3115/1117822.1117837

ABSTRACT

This paper shows that linguistic techniques along with machine learning can extract high quality noun phrases for the purpose of providing the gist or summary of email messages. We describe a set of comparative experiments using several machine learning algorithms for the task of salient noun phrase extraction. Three main conclusions can be drawn from this study: (i) the modifiers of a noun phrase can be semantically as important as the head for the task of gisting, (ii) linguistic filtering improves the performance of machine learning algorithms, (iii) a combination of classifiers improves accuracy.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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B. Boguraev and C. Kennedy. 1999. Salience-based content characterisation of text documents. In Interjit Mani and T. Maybury, Mark, editors, Advances in Automatic Text Summarization, pages 99--111. The MIT Press.
 
3
W. Cohen. 1995. Fast effective rule induction. In Machine-Learning: Proceedings of the Twelfth International Conference.
 
4
 
5
J. Justeson and S. Katz. 1995. Technical terminology: Some linguistic properties and an algorithm for identification in text. Natural Language Engineering, (1):9--27.
 
6
J. L. Klavans, M. S. Chodorow, and N. Wacholder. 1990. From dictionary to knowledge base via taxonomy. In Proceedings of the Sixth Conference of the University of Waterloo Centre for the New Oxford English Dictionary and Text Research: Electronic Text Research, University of Waterloo, Canada.
7
 
8
R. J Mooney and C. Cardie. 1999. Symbolic machine learning for natural language processing. In ACL'99 Tutorial.
 
9
S. K. Murthy, S. Kasif, S. Salzberg, and R. Beigel. 1993. OCI: Randomized induction of oblique decision trees. In Proceedings of the Eleventh National Conference on Artificial Intelligence, pages 322--327, Washington, D.C.
 
10
 
11
L. A. Ramshaw and M. P. Marcus. 1995. Text chunking using transformation-based learning. In Proceedings of Third ACL Workshop on Very Large Corpora.
 
12
A. Smeaton. 1999. Using NLP or NLP resources for information retrieval tasks. In Tomek Strzalkowski, editor, Natural Language Information Retrieval. Kluwer, Boston, MA.
 
13
K. Sparck Jones. 1999. What is the role for NLP in text retrieval. In Tomek Strzalkowski, editor, Natural Language Information Retrieval, pages 1--12. Kluwer, Boston, MA.
 
14
T. Strzalkowski, F. Lin, J. Wang, and J. Perez-Carballo. 1999. Evaluating natural language processing techniques in information retrieval. In Tomek Strzalkowski, editor, Natural Language Information Retrieval. Kluwer, Boston, MA.
 
15
 
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17
N. Wacholder. 1998. Simplex NPS sorted by head: A method for identifying significant topics within a document. In Proceedings of the COLING-ACL Workshop on the Computational Treatment of Nominals, Montreal, Canada.
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CITED BY  8
Collaborative Colleagues:
Smaranda Muresan: colleagues
Evelyne Tzoukermann: colleagues
Judith L. Klavans: colleagues