Uncertainty Detection in Natural Language Texts

Vincze Veronika
Uncertainty Detection in Natural Language Texts.
Doctoral thesis (PhD), University of Szeged.
(2015) (Unpublished)

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Abstract in foreign language

Uncertainty is an important linguistic phenomenon that is relevant in many fields of language processing. In its most general sense, it can be interpreted as lack of information: the hearer or the reader cannot be certain about some pieces of information. Thus, uncertain propositions are those whose truth value or reliability cannot be determined due to lack of information. Distinguishing between factual (i.e. true or false) and uncertain propositions is of primary importance both in linguistics and natural language processing applications. For instance, in information extraction an uncertain piece of information might be of some interest for an end-user as well, but such information must not be confused with factual textual evidence (reliable information) and the two should be kept separated. The main objective of this thesis is to detect uncertainty in English and Hungarian natural language texts. As opposed to earlier studies that focused on specific domains and were English-oriented, we will offer here a comprehensive approach to uncertainty detection, which can be easily adapted to the specific needs of many domains and languages. In our investigations, we will pay attention to create linguistically plausible models of uncertainty that will be exploited in creating manually annotated corpora that will serve as the base for the implementation of our uncertainty detectors for several domains, with the help of supervised machine learning techniques. Furthermore, we will also demonstrate that uncertainty detection can be fruitfully applied in a real-world application, namely, information extraction from clinical discharge summaries.

Item Type: Thesis (Doctoral thesis (PhD))
Creators: Vincze Veronika
Hungarian title: Bizonytalanság azonosítása természetes nyelvű szövegekben
Position, academic title, institution
MTMT author ID
Csirik János
DSc, egyetemi tanár, SZTE TTIK Számítógépes Algoritmusok és Mesterséges Intelligencia Tanszék
Subjects: 01. Natural sciences > 01.02. Computer and information sciences
Divisions: Doctoral School of Computer Science
Discipline: Engineering > Information Technology
Language: English
Date: 2015. June 24.
Item ID: 2291
MTMT identifier of the thesis: 2804039
doi: https://doi.org/10.14232/phd.2291
Date Deposited: 2014. Jul. 04. 22:38
Last Modified: 2020. Jul. 14. 09:34
Depository no.: B 5902
URI: https://doktori.bibl.u-szeged.hu/id/eprint/2291
Defence/Citable status: Defended.

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