Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
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. IntroductionThe Internet has changed the news industry (The Economist, 2011). Most newspapers and news agencies provide news on their web pages. News portals work as a news aggregator and gather, merge, and organize news articles obtained from various sources. Multisource news portals provide various advantages such as richness in news content and an opportunity to follow event developments from different perspectives. In addition, it is practical to Received May 20, 2011; revised September 12, 2011; accepted September 28, 2011 1 Present address: Computer Science Department, New Jersey Institute of Technology, University Heights Newark, NJ 07102.© 2011 ASIS&T • Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/asi.21697 follow different news sources from a single web page. Google News (http://news.google.com) is a well-known commercial news portal example. It offers many services such as information retrieval, personalized information filtering, and news clustering. Research-oriented examples include NewsBlaster (McKeown et al., 2002) and NewsInEssence (Radev, Otterbacher, Winkel, & Blair-Goldensohn, 2005), each of which provides clustering and summarization services over the news.As the number of sources and events increase, news readers may be overloaded with information and thus may face difficulty in finding news related to their interests. Different organizational techniques have been employed for more effective, efficient, and enjoyable browsing. Studies on new-event detection and topic tracking aim to organize news with respect to events or topics. In topic detection and trackin...
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. IntroductionThe Internet has changed the news industry (The Economist, 2011). Most newspapers and news agencies provide news on their web pages. News portals work as a news aggregator and gather, merge, and organize news articles obtained from various sources. Multisource news portals provide various advantages such as richness in news content and an opportunity to follow event developments from different perspectives. In addition, it is practical to Received May 20, 2011; revised September 12, 2011; accepted September 28, 2011 1 Present address: Computer Science Department, New Jersey Institute of Technology, University Heights Newark, NJ 07102.© 2011 ASIS&T • Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/asi.21697 follow different news sources from a single web page. Google News (http://news.google.com) is a well-known commercial news portal example. It offers many services such as information retrieval, personalized information filtering, and news clustering. Research-oriented examples include NewsBlaster (McKeown et al., 2002) and NewsInEssence (Radev, Otterbacher, Winkel, & Blair-Goldensohn, 2005), each of which provides clustering and summarization services over the news.As the number of sources and events increase, news readers may be overloaded with information and thus may face difficulty in finding news related to their interests. Different organizational techniques have been employed for more effective, efficient, and enjoyable browsing. Studies on new-event detection and topic tracking aim to organize news with respect to events or topics. In topic detection and trackin...
Text reuse is a common phenomenon in a variety of usergenerated content. Along with the quick expansion of social media, reuses of local text are occurring much more frequently than ever before. The task of detecting these local reuses serves as an essential step for many applications. It has attracted extensive attention in recent years. However, semantic level similarities have not received consideration in most previous works. In this paper, we introduce a novel method to efficiently detect local reuses at the semantic level for large scale problems. We propose to use continuous vector representations of words to capture the semantic level similarities between short text segments. In order to handle tens of billions of documents, methods based on information geometry and hashing methods are introduced to aggregate and map text segments presented by word embeddings to binary hash codes. Experimental results demonstrate that the proposed methods achieve significantly better performance than state-of-the-art approaches in all six document collections belonging to four different categories. At some recall levels, the precisions of the proposed method are even 10 times higher than previous methods. Moreover, the efficiency of the proposed method is comparable to or better than that of some other hashing methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.