Browsing by Subject "Test Collection"

Now showing 1 - 5 of 5

Open Access
CoDet : a new algorithm for containment and near duplicate detection in text corpora
(2012) Varol, Emre
In this thesis, we investigate containment detection, which is a generalized version of the well known near-duplicate detection problem concerning whether a document is a subset of another document. In text-based applications, there are three way of observing document containment: exact-duplicates, near-duplicates, or containments, where first two are the special cases of containment. To detect containments, we introduce CoDet, which is a novel algorithm that focuses particularly on containment problem. We also construct a test collection using a novel pooling technique, which enables us to make reliable judgments for the relative effectiveness of algorithms using limited human assessments. We compare its performance with four well-known near duplicate detection methods (DSC, full fingerprinting, I-Match, and SimHash) that are adapted to containment detection. Our algorithm is especially suitable for streaming news. It is also expandable to different domains. Experimental results show that CoDet mostly outperforms the other algorithms and produces remarkable results in detection of containments in text corpora.
Open Access
CoDet: Sentence-based containment detection in news corpora
(ACM, 2011) Varol, Emre; Can, Fazlı; Aykanat, Cevdet; Kaya, Oğuz
We study a generalized version of the near-duplicate detection problem which concerns whether a document is a subset of another document. In text-based applications, document containment can be observed in exact-duplicates, near-duplicates, or containments, where the first two are special cases of the third. We introduce a novel method, called CoDet, which focuses particularly on this problem, and compare its performance with four well-known near-duplicate detection methods (DSC, full fingerprinting, I-Match, and SimHash) that are adapted to containment detection. Our method is expandable to different domains, and especially suitable for streaming news. Experimental results show that CoDet effectively and efficiently produces remarkable results in detecting containments. © 2011 ACM.
Open Access
Developing a text categorization template for Turkish news portals
(IEEE, 2011) Toraman, Çağrı; Can, Fazlı; Koçberber, Seyit
In news portals, text category information is needed for news presentation. However, for many news stories the category information is unavailable, incorrectly assigned or too generic. This makes the text categorization a necessary tool for news portals. Automated text categorization (ATC) is a multifaceted difficult process that involves decisions regarding tuning of several parameters, term weighting, word stemming, word stopping, and feature selection. In this study we aim to find a categorization setup that will provide highly accurate results in ATC for Turkish news portals. We also examine some other aspects such as the effects of training dataset set size and robustness issues. Two Turkish test collections with different characteristics are created using Bilkent News Portal. Experiments are conducted with four classification methods: C4.5, KNN, Naive Bayes, and SVM (using polynomial and rbf kernels). Our results recommends a text categorization template for Turkish news portals and provides some future research pointers. © 2011 IEEE.
Open Access
New event detection and topic tracking in Turkish
(John Wiley & Sons, Inc., 2010) Can, F.; Kocberber, S.; Baglioglu, O.; Kardas, S.; Ocalan, H. C.; Uyar, E.
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.
Open Access
Redif extraction in handwritten Ottoman literary texts
(IEEE, 2010) Can, Ethem F.; Duygulu, Pınar; Can, Fazlı; Kalpaklı, Mehmet
Repeated patterns, rhymes and redifs, are among the fundamental building blocks of Ottoman Divan poetry. They provide integrity of a poem by connecting its parts and bring a melody to its voice. In Ottoman literature, poets wrote their works by making use of the rhymes and redifs of previous poems according to the nazire (creative imitation) tradition either to prove their expertise or to show respect towards old masters. Automatic recognition of redifs would provide important data mining opportunities in literary analyses of Ottoman poetry where the majority of it is in handwritten form. In this study, we propose a matching criterion and method, Redif Extraction using Contour Segments (RECS) using the proposed matching criterion, that detects redifs in handwritten Ottoman literary texts using only visual analysis. Our method provides a success rate of 0.682 in a test collection of 100 poems. © 2010 IEEE.