Dissertation/Thesis Abstract

Statistical information retrieval models: Experiments, evaluation on real time data
by Rao, Ashwani Pratap, M.S., University of Delaware, 2014, 81; 1567821
Abstract (Summary)

We are all aware of the rise of information age: heterogeneous sources of information and the ability to publish rapidly and indiscriminately are responsible for information chaos. In this work, we are interested in a system which can separate the "wheat" of vital information from the chaff within this information chaos. An efficient filtering system can accelerate meaningful utilization of knowledge. Consider Wikipedia, an example of community-driven knowledge synthesis. Facts about topics on Wikipedia are continuously being updated by users interested in a particular topic. Consider an automatic system (or an invisible robot) to which a topic such as "President of the United States" can be fed. This system will work ceaselessly, filtering new information created on the web in order to provide the small set of documents about the "President of the United States" that are vital to keeping the Wikipedia page relevant and up-to-date. In this work, we present an automatic information filtering system for this task. While building such a system, we have encountered issues related to scalability, retrieval algorithms, and system evaluation; we describe our efforts to understand and overcome these issues.

Indexing (document details)
Advisor: Carterette, Benjamin A.
Commitee:
School: University of Delaware
Department: Department of Computer and Information Sciences
School Location: United States -- Delaware
Source: MAI 54/01M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Computer science
Keywords: Information filtering, Information retrieval, Modeling, Natural language processing, Search, Statistical information retrieval
Publication Number: 1567821
ISBN: 9781321291254
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