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dc.contributor.authorTan, Wee Beng.-
dc.description.abstractDetecting events from one or more temporally-ordered stream(s) of documents (e.g. news articles, blog posts) and group these documents based on the events that they describe is one of the goals in Topic Detection and Tracking (TDT). However, most of the existing event detection solutions do not consider users’ input (e.g. search engines, blog posts) and group news articles into events which may not be useful to users. In this project, the author studied the approach of query-guided event detection and tracking from two parallel documents streams (news and blog) based on an ongoing research work. This approach takes users’ input into consideration through popular keyword queries and group queries, news articles and blog posts into events. The main focus in the project is to build an annotated dataset using real-world data collected from Google News and Technorati for evaluating the event detection algorithms. A web application was developed to facilitate the tasks of annotating, searching, analyzing and manipulating the dataset. Various software, tools and APIs were explored to aid in the development of a user friendly and interactive web interface.en_US
dc.format.extent89 p.en_US
dc.rightsNanyang Technological University-
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Document and text processingen_US
dc.titleLink blog post to news articlesen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorSun Aixinen_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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Updated on Dec 8, 2021


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