Unit:
Department of Informatics and TelecommunicationsLibrary of the School of Science
Dissertation committee:
Κωνσταντίνος Χαλάτσης Καθηγητής Επιβλέπων, Κωνσταντίνος Σπυρόπουλος Διευθυντής Έρευνας, Παναγιώτης Σταματόπουλος Επίκουρος Καθηγητής
Original Title:
Μηχανική Μάθηση στην Επεξεργασία Φυσικής Γλώσσας
Summary:
This thesis examines the use of machine learning techniques in various tasks of
natural language processing, mainly for the task of information extraction from
texts. The objectives are the improvement of adaptability of information
extraction systems to new thematic domains (or even languages), and the
improvement of their performance using as fewer resources (either linguistic or
human) as possible. This thesis has examined two main axes: a) the research and
assessment of existing algorithms of machine learning mainly in the stages of
linguistic pre-processing (such as part of speech tagging) and named-entity
recognition, and b) the creation of a new machine learning algorithm and its
assessment on synthetic data, as well as in real world data from the task of
relation extraction between named entities. This new algorithm belongs to the
category of inductive grammar learning, and can infer context free grammars
from positive examples only.
Keywords:
Ιnformation extraction, Μachine learning, Grammatical inference, Grammatical learning, Grammar
Number of index pages:
193-196
Number of references:
146
