Incremental aspect models for mining document streams

Arun C. Surendran; Suvrit Sra

doi:10.1007/11871637_65

Incremental aspect models for mining document streams

Arun C. Surendran, Suvrit Sra

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

5 Scopus citations

Abstract

In this paper we introduce a novel approach for incrementally building aspect models, and use it to dynamically discover underlying themes from document streams. Using the new approach we present an application which we call "query-line tracking" i.e., we automatically discover and summarize different themes or stories that appear over time, and that relate to a particular query. We present evaluation on news corpora to demonstrate the strength of our method for both query-line tracking, online indexing and clustering.

Original language	English
Title of host publication	Knowledge Discovery in Databases
Subtitle of host publication	PKDD 2006 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings
Publisher	Springer Verlag
Pages	633-640
Number of pages	8
ISBN (Print)	3540453741, 9783540453741
DOIs	https://doi.org/10.1007/11871637_65
State	Published - 2006
Externally published	Yes
Event	10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006 - Berlin, Germany Duration: 18 Sep 2006 → 22 Sep 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4213 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006
Country/Territory	Germany
City	Berlin
Period	18/09/06 → 22/09/06

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10.1007/11871637_65

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Surendran, A. C., & Sra, S. (2006). Incremental aspect models for mining document streams. In Knowledge Discovery in Databases: PKDD 2006 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings (pp. 633-640). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4213 LNAI). Springer Verlag. https://doi.org/10.1007/11871637_65

Surendran, Arun C. ; Sra, Suvrit. / Incremental aspect models for mining document streams. Knowledge Discovery in Databases: PKDD 2006 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. Springer Verlag, 2006. pp. 633-640 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Incremental aspect models for mining document streams",

abstract = "In this paper we introduce a novel approach for incrementally building aspect models, and use it to dynamically discover underlying themes from document streams. Using the new approach we present an application which we call {"}query-line tracking{"} i.e., we automatically discover and summarize different themes or stories that appear over time, and that relate to a particular query. We present evaluation on news corpora to demonstrate the strength of our method for both query-line tracking, online indexing and clustering.",

author = "Surendran, {Arun C.} and Suvrit Sra",

year = "2006",

doi = "10.1007/11871637_65",

language = "English",

isbn = "3540453741",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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booktitle = "Knowledge Discovery in Databases",

note = "10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006 ; Conference date: 18-09-2006 Through 22-09-2006",

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Surendran, AC & Sra, S 2006, Incremental aspect models for mining document streams. in Knowledge Discovery in Databases: PKDD 2006 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4213 LNAI, Springer Verlag, pp. 633-640, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006, Berlin, Germany, 18/09/06. https://doi.org/10.1007/11871637_65

Incremental aspect models for mining document streams. / Surendran, Arun C.; Sra, Suvrit.
Knowledge Discovery in Databases: PKDD 2006 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. Springer Verlag, 2006. p. 633-640 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4213 LNAI).

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

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AB - In this paper we introduce a novel approach for incrementally building aspect models, and use it to dynamically discover underlying themes from document streams. Using the new approach we present an application which we call "query-line tracking" i.e., we automatically discover and summarize different themes or stories that appear over time, and that relate to a particular query. We present evaluation on news corpora to demonstrate the strength of our method for both query-line tracking, online indexing and clustering.

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Surendran AC, Sra S. Incremental aspect models for mining document streams. In Knowledge Discovery in Databases: PKDD 2006 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. Springer Verlag. 2006. p. 633-640. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11871637_65

Incremental aspect models for mining document streams

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