Browsing by Subject "Topic tracking"
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Item Open Access Diyalog tabanlı metinlerde konu değişimi tespiti(IEEE, 2019-04) Şenel, Lütfi Kerem; Yücesoy, Veysel; Koҫ, A.; Çukur, TolgaSon dönemde katlanarak gelişen haberleşme yöntemleri (internet, sosyal medya, akıllı telefon, vb.) sayesinde veriye ulaşmak ve paylaşmak kolaylaşmıştır. Özellikle son yıllarda sözlü ve yazılı paylaşım mecraları hızlı gelişim göstermiştir. Yazılı paylaşımın en hızlı yaşandıgı alanlar arasında sosyal medya siteleri ve forumlar öne çıkmaktadır. Forumlarda sosyal medyadan farklı olarak, her başlık altında sadece o başlık ile ilgili konuşmalar yapılması beklenmektedir. Konu kısıtlılıgı olan ve sözlü iletişimin son yıllarda en hızlı geli¸stigi alanlardan biri de çagrı merkezleridir. Belirli konuların dışına çıkılması ya da ana konunun değiştirilmesinin otomatik tespiti özellikle çağrı merkezleri ve teknik forumlar gibi mecraların iletişim performansının değerlendirilmesi ve otomatik olarak yönetilebilmesi açısından önemlidir. Bu çalışma ile diyalog tabanlı Türkçe metinler içerisinde konu değişimini otomatik olarak algılayabilen sınıflandırıcılar geliştirilmiştir. Bu sınıflandırıcıların geliştirilebilmesi için öncelikle Türkçe forumlardan konu tabanlı karşılıklı konuşma verileri tasnif edilerek ham bir veri kümesi elde edilmiştir. Oluşturulan veri kümesi üzerinde klasik bir yöntem (TF-IDF) ile bir derin öğrenme modeli (LSTM) otomatik konu değişimi tespiti problemi için karşılaştırılmıştır. Klasik yöntem ile test kümesinde %80’lere varan başarı elde edilirken, derin öğrenme yönteminin performansının %76 seviyesinde kaldığı gözlenmiştir.Item 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.Item Open Access Novelty detection for topic tracking(John Wiley & Sons, Inc., 2012) Aksoy, C.; Can, F.; Kocberber, S.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 called BilNov-2005 and 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 categorybased threshold learning achieves promising results when compared to general threshold learning. © 2011 ASIS&T.Item Open Access Topic tracking using chronological term ranking(2013-10) Acun, Bilge; Başpınar, Alper; Oǧuz, Ekin; Saraç, M.İlker; Can, FazlıTopic tracking (TT) is an important component of topic detection and tracking (TDT) applications. TT algorithms aim to determine all subsequent stories of a certain topic based on a small number of initial sample stories. We propose an alternative similarity measure based on chronological term ranking (CTR) concept to quantify the relatedness among news articles for topic tracking. The CTR approach is based on the fact that in general important issues are presented at the beginning of news articles. By following this observation we modify the traditional Okapi BM25 similarity measure using the CTR concept. Using a large standard test collection we show that our method provides a statistically significantly improvement with respect to the Okapi BM25 measure. The highly successful performance indicates that the approach can be used in real applications. © 2013 Springer-Verlag London.