Scalable hierarchical topic detection: exploring a sample based approach

Authors:
Dolf Trieschnigg;Wessel Kraaij
Affiliations:
University of Twente, Enschede, The Netherlands;TNO, Delft, The Netherlands
Venue:
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2005

Citing 5
Cited 2

Scatter/Gather: a cluster-based approach to browsing large document collections

SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Data clustering: a review

ACM Computing Surveys (CSUR)
Document clustering with committees

SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Flexible intrinsic evaluation of hierarchical clustering for TDT

CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
An architecture for efficient document clustering and retrieval on a dynamic collection of newspaper texts

IRSG'98 Proceedings of the 20th Annual BCS-IRSG conference on Information Retrieval Research

Finding and linking incidents in news

Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Fine-grained topic detection in news search results

Proceedings of the 27th Annual ACM Symposium on Applied Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Hierarchical topic detection is a new task in the TDT 2004 evaluation program, which aims to organize an unstructured news collection in a directed acyclic graph (DAG) structure, reflecting the topics discussed. We present a scalable architecture for HTD and compare several alternative choices for agglomerative clustering and DAG optimization in order to minimize the HTD cost metric.