TopCat: Data Mining for Topic Identification in a Text Corpus

TopCat: Data Mining for Topic Identification in a Text Corpus (2002)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Chris Clifton , Robert Cooley , Jason Rennie

Venue:	In Proceedings of the 3rd European Conference of Principles and Practice of Knowledge Discovery in Databases
Citations:	27 - 4 self

BibTeX

@INPROCEEDINGS{Clifton02topcat:data,
    author = {Chris Clifton and Robert Cooley and Jason Rennie},
    title = {TopCat: Data Mining for Topic Identification in a Text Corpus},
    booktitle = {In Proceedings of the 3rd European Conference of Principles and Practice of Knowledge Discovery in Databases},
    year = {2002}
}

Years of Citing Articles

Bookmark

OpenURL

Abstract

TopCat (Topic Categories) is a technique for identifying topics that recur in articles in a text corpus. Natural language processing techniques are used to identify key entities in individual articles, allowing us to represent an article as a set of items. This allows us to view the problem in a database/data mining context: Identifying related groups of items. This paper presents a novel method for identifying related items based on "traditional" data mining techniques. Frequent itemsets are generated from the groups of items, followed by clusters formed with a hypergraph partitioning scheme. We present an evaluation against a manually-categorized "ground truth" news corpus showing this technique is effective in identifying "topics" in collections of news articles.

Citations

2699	MJ: Introduction to Modern Information Retrieval - Salton, McGill - 1986
2159	Fast algorithms for mining association rules - Agrawal, Srikant - 1994
1953	Mining association rules between sets of items in large databases - Agrawal, Imielinski, et al. - 1993
1459	An algorithm for suffix stripping - Porter - 1980
1368	Text categorization with support vector machines - Joachims - 1998
1302	WordNet: an online lexical database - Miller, Beckwith, et al. - 1990
739	A Comparative Study on Feature Selection in Text Categorization - Yang
627	Word association norms, mutual information, and lexicography - Church, Hanks - 1990
444	Computational Analysis of Present Day American English - Kucera, Francis - 1967
414	Beyond market baskets: Generalizing association rules to correlations - Brin, Motwani, et al. - 1997
399	Agrawal R: Mining generalized association rules - Srikant - 1995
199	Multilevel hypergraph partitioning: Application in VLSI domain - Karypis - 1997
196	O.: Grouper: A dynamic clustering interface to Web search results - Zamir, Etzioni - 1999
168	The first text retrieval conference - Harman - 1993
162	Mining Association Rules Between Sets of Items - Agrawal, Imielminski, et al. - 1993
106	Automatic structuring and retrieval of large text files - Salton, Allan, et al. - 1994
102	Information extraction as a basis for high-precision text classification - Riloff, Lehnert - 1994
91	Transmission of Information - Fano - 1949
87	Fast and intuitive clustering of web documents - Zamir, Etzioni, et al. - 1997
87	Raghavan P: Scalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomy - Chakrabarti, AR - 1998
80	Clustering based on association rule hypergraphs - Han, Karypis, et al. - 1997
79	Natural language processing for information retrieval - Lewis, Jones - 1996
76	Evaluating text categorization - Lewis - 1991
62	An efficient algorithm for the incremental updation of association rules in large databases - Thomas, Bodagala, et al. - 1997
57	A method for word sense disambiguation of unrestricted text - Mihalcea, Moldovan - 1999
55	Multilevel hypergraph partitioning - Karypis, Aggarwal, et al. - 1997
55	Clustering transactions using large items - Wang, Xu, et al. - 1999
47	Discovering trends in text databases - Lent, Agrawal, et al. - 1997
36	Retrieval performance in FERRET: A conceptual information retrieval system - Mauldin
34	Efficient algorithms for discovering frequent sets in incremental databases - Feldman, Aumann, et al. - 1997
33	An investigation of linguistic features and clustering algorithms for topical document clustering - Hatzivassiloglou, Gravano, et al.
32	An algorithm for su±x stripping - Porter - 1997
25	A WordNetbased algorithm for word sense disambiguation - Li, Szpakowicz, et al. - 1995
24	Methods of automatic term recognition: A review - Kageura, Umino - 1996
21	Maximal association rules: a new tool for mining for keyword cooccurrences in document collections - Feldman, Aumann, et al. - 1997
21	Machine learning of event segmentation for news on demand - Boykin, Merlino - 2000
19	Generating association rules from semi-structured documents using an extended concept hierarchy - Singh, Scheuermann, et al. - 1997
19	Rajeev Motwani, Svetlozar Nestorov, and Arnon Rosenthal, “Query flocks: A generalization of association rule mining - Tsur, Ullman, et al.
18	Visualizing the full spectrum of document relationships - Hetzler, Harris, et al. - 1998
18	Query Flocks: A Generalization of Association Rule Mining - Tsur, Ullman, et al. - 1998
16	Language-oriented information retrieval - Lewis, Croft, et al. - 1989
13	Methods of Automatic Term Recognition - Kageura, Umino - 1996
12	Mixed Initiative Development of Language Processing Systems - Day, Aberdeen, et al. - 1997
12	Exploiting background information in knowledge discovery from text - Feldman, Hirsh - 1998
11	Classification of news stories using support vector machines - Cooley
11	Motwani R: Beyond market baskets: Generalizing association rules to dependence rules. Data Mining and Knowledge Discovery - Silverstein, Brin - 1998
10	Inkeri Verkamo, “Mining in the phrasal frontier - Ahonen, Heinonen, et al. - 1997
10	Link based clustering of web search results - Wang, Kitsuregawa
9	Efficient mining of association rules in text databases - Holt, Chung
8	Information extraction as a basis for high-precision text classi cation - Rilo, Lehnert - 1994