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Profiling for belief acquisition from reported speech

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Profiling for belief acquisition from reported speech

Doandes, Monia (2003) Profiling for belief acquisition from reported speech. Masters thesis, Concordia University.

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Abstract

We are in the Internet era. Now, more than ever, there is an excessive amount of documents available online, making it harder and harder to find the ones that are relevant to our searches. The new systems, which use belief models to improve the understanding of what the user really wants, need a practical text representation model that can make the extraction of beliefs easier. Profiling is an appropriate method for creating such a text representation. In this thesis we develop the details for a process of building basic profiles from reported speech in newspaper articles . This process recognizes and extracts reported speech, and determines the internal structure of reported speech clauses, fitting it within basic profile frames. In order to accomplish this, three building blocks are used: recognition of reporting verbs, punctuation analysis and the structure of the reported speech . We implemented a fully automated system which extracts basic profiles and analyzes them, differentiating between circumstantial information and primary information. Inside the primary information, a differentiation is made between direct quotes and paraphrases. The source, reporting verb and other circumstantial information are extracted. The reporting verb is analyzed, determining its tense, aspect, modality, mood, voice and polarity. The semantic dimensions of the reporting verb are also analyzed, paving the path for a future belief analysis based on reporting verbs.

Divisions:Concordia University > Faculty of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Doandes, Monia
Pagination:ix, 136 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:Theses (M.Comp.Sc.)
Program:Computer Science and Software Engineering
Date:2003
Thesis Supervisor(s):Bergler, Sabine
ID Code:2068
Deposited By:Concordia University Libraries
Deposited On:27 Aug 2009 13:24
Last Modified:08 Dec 2010 10:24
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