New event detection and topic tracking in Turkish
Date
2010Source Title
Journal of the American Society for Information Science and Technology
Print ISSN
2330-1635
Publisher
John Wiley & Sons, Inc.
Volume
61
Issue
4
Pages
802 - 819
Language
English
Type
ArticleItem Usage Stats
149
views
views
296
downloads
downloads
Abstract
Topic detection and tracking (TDT) applications aim to organize the temporally ordered stories of a news stream according to the events. Two major problems in TDT are new event detection (NED) and topic tracking (TT). These problems focus on finding the first stories of new events and identifying all subsequent stories on a certain topic defined by a small number of sample stories. In this work, we introduce the first large-scale TDT test collection for Turkish, and investigate the NED and TT problems in this language. We present our test-collection-construction approach, which is inspired by the TDT research initiative. We show that in TDT for Turkish with some similarity measures, a simple word truncation stemming method can compete with a lemmatizer-based stemming approach. Our findings show that contrary to our earlier observations on Turkish information retrieval, in NED word stopping has an impact on effectiveness. We demonstrate that the confidence scores of two different similarity measures can be combined in a straightforward manner for higher effectiveness. The influence of several similarity measures on effectiveness also is investigated. We show that it is possible to deploy TT applications in Turkish that can be used in operational settings. © 2010 ASIS&T.
Keywords
Confidence scoreEvent detection
Number of samples
Research initiatives
Similarity measure
Test Collection
Topic detection and tracking
Topic tracking
Turkishs
Information services