Cohesion-Model is a new Story Link Detection (SLD) System that is inspired by the relevance model of TDT. Task in hand is to detect whether given two documents are linked. Each document is expanded using corresponding relevant documents. Each term in the relevant document is weighted according to the cohesion factor. The two models then are compared using the modified fractional similarity. Per...

84.6% of touching digit strings have only two digits touching, 12.3% have three digits touching, and 3.1% have more than three digits touching. We present a multiexperts approach to recognize touching digit pairs (TDP) and touching digit triples (TDT). We combine holistic and traditional segmentation methods. 25,686 TDP training samples and 2778 TDP testing samples collected from USPS mail are ...

The main purpose of topic detection and tracking (TDT) is to detect, group, and organize newspaper articles reporting on the same event. Since an event is a reported occurrence at a specific time and place, and the unavoidable consequences, it is conceivable that place information in a news article plays an important role in TDT. We analyzed news articles for their characteristics of place info...

We discuss Topic Detection, a sub-task of the Topic Detection and Tracking (TDT) Project, and present initial results for both a baseline system and a set of extensions that attempt to model events and reportage in the news domain. Moreover, we describe a system that clusters live news feeds and presents this data to the user in a variety of formats. We conclude that this system produces intere...

We describe a topic tracking system developed at GE R&D Center in connection with our participation in DARPA TDT evaluations. The TDT tracking task is specified as follows: given Nt training news stories on a topic, the system must find all subsequent stories on the same topic in all tracked news sources. These sources include radio and television news broadcasts, as well as newswire feeds. The...

We extend the event tracking task of Topic Detection and Tracking (TDT) to create a framework in which a user can highlight relevant passages in addition to specifying the relevance of documents. A dual framework of combined document and passage feedback improves performance over a state-of-the-art system without feedback by over 70% . Although annotators vary in the content and length of the p...

Abstract Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative concerned with finding groups of stories on the same topic (tdt, 1998). The goal is to build systems that can segment, detect, and track incoming news stories (possibly from multiple continuous feeds) with respect to pre-defined topics. While the detection task detects the first story on a particular topic, the tracking...

Topic detection and tracking approaches monitor broadcast news in order to spot new, previously unreported events and to track the development of the previously spotted ones. The dynamical nature of the events makes the use of state-of-the-art methods difficult. We present a new topic definition that has potential to model evolving events. We also discuss incorporating ontologies into the simil...

The main purpose of topic detection and tracking (TDT) is to detect, group, and organize newspaper articles reporting on the same event. Since an event is a reported occurrence at a specific time and place and the unavoidable consequences, TDT can benefit from an explicit use of time and place information. In this work, we focused on place information, using time information as in the previous ...

In computer graphics (CG), making photorealistic images using a computer is now commonplace. As a result, directors can create convincing, imaginary worlds, and designers can virtually prototype, visualize, and evaluate potential products and spaces. In order to achieve these purposes, rendering methods such as ray tracing and radiosity rendering methods have been developed. However, the render...