New event detection and tracking in Turkish

Abstract

The amount of information and the number of information resources on the Internet have been growing rapidly for over a decade. This is also true for on-line news and news providers. To overcome information overload news consumers prefer to track the topics that they are interested in. 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 respectively focus on finding the first stories of previously unseen new events and all subsequent stories on a certain topic defined by a small number of initial stories. In this thesis, the NED and TT problems are investigated in detail using the first large-scale test collection (BilCol2005) developed by Bilkent Information Retrieval Group. The collection contains 209,305 documents from the entire year of 2005 and involves several events in which eighty of them are annotated by humans. The experimental results show that a simple word truncation stemming method can statistically compete with a sophisticated stemming approach that pays attention to the morphological structure of the language. Our statistical findings illustrate that word stopping and the contents of the associated stopword list are important and removing the stopwords from content can significantly improve the system performance. We demonstrate that the confidence scores of two different similarity measures can be combined in a straightforward manner for improving the effectiveness.