Abstract Multisource web news portals provide various advantages such as richness in news content and an opportunity to follow developments from different perspectives. However, in such environments, news variety and quantity can have an overwhelming effect. New‐event detection and topic‐tracking studies address this problem. They examine news streams and organize stories according to their events; however, several tracking stories of an event/topic may contain no new information (i.e., no novelty). We study the novelty detection (ND) problem on the tracking news of a particular topic. For this purpose, we build a Turkish ND test collection calledBilNov‐2005and propose the usage of three ND methods: a cosine‐similarity (CS)‐based method, a language‐model (LM)‐based method, and a cover‐coefficient (CC)‐based method. For the LM‐based ND method, we show that a simpler smoothing approach, Dirichlet smoothing, can have similar performance to a more complex smoothing approach, Shrinkage smoothing. We introduce a baseline that shows the performance of a system with random novelty decisions. In addition, a category‐based threshold learning method is used for the first time in ND literature. The experimental results show that the LM‐based ND method significantly outperforms the CS‐ and CC‐based methods, and category‐based threshold learning achieves promising results when compared to general threshold learning.

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.

No abstract available.

Abstract In this study, we investigate information retrieval (IR) on Turkish texts using a large‐scale test collection that contains 408,305 documents and 72 ad hoc queries. We examine the effects of several stemming options and query‐document matching functions on retrieval performance. We show that a simple word truncation approach, a word truncation approach that uses language‐dependent corpus statistics, and an elaborate lemmatizer‐based stemmer provide similar retrieval effectiveness in Turkish IR. We investigate the effects of a range of search conditions on the retrieval performance; these include scalability issues, query and document length effects, and the use of stopword list in indexing.

We present the results of the first large-scale Turkish information retrieval experiments performed on a TREC-like test collection. The test bed, which has been created for this study, contains 95.5 million words, 408,305 documents, 72 ad hoc queries and has a size of about 800MB. All documents come from the Turkish newspaper Milliyet. We implement and apply simple to sophisticated stemmers and various query-document matching functions and show that truncating words at a prefix length of 5 creates an effective retrieval environment in Turkish. However, a lemmatizer-based stemmer provides significantly better effectiveness over a variety of matching functions.

For signature files we propose a new false drop estimation method for databases with varying record lengths. Our approach provides more accurate estimation of the number of false drops by considering the lengths of individual records instead of using the average number of terms per record. In signature file processing, accurate estimation of the number of false drops is essential to obtain a more accurate signature file and therefore to obtain a better (query) response time. With a formal proof we show that under certain conditions the number of false drops estimated by considering the average record length is less than or equal to the precise 'expected' estimation which is based on the individual record lengths. The experiments with real data show that the proposed method accurately estimates the number of false drops and the actual response time. Depending on the space overhead, our approach obtains up to 33% and 20% response time improvements for the conventional sequential and new efficient multiframe signature file methods, respectively.