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.

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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 cluster-based retrieval (CBR) experiments on the largest available Turkish document collection. Our experiments evaluate retrieval effectiveness and efficiency on both an automatically generated clustering structure and a manual classification of documents. In particular, we compare CBR effectiveness with full-text search (FS) and evaluate several implementation alternatives for CBR. Our findings reveal that CBR yields comparable effectiveness figures with FS. Furthermore, by using a specifically tailored cluster-skipping inverted index we significantly improve in-memory query processing efficiency of CBR in comparison to other traditional CBR techniques and even FS.

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.