Thesis (MSc)--University of Stellenbosch, 2004. / ENGLISH ABSTRACT: We present a system that uses statistical machine learning to identify and extract
bibliography information from scientific literature. Techniques for finding and gathering
useful information from the ever growing volume of knowledge on the World Wide Web
(WWW), are investigated.
We use hidden Markov models both for recognition of bibliography styles and extraction
of bibliographic information with an accuracy of up to 97%. The accuracy with which
we are able to extract this information allows us to present a case study in which
we apply methods of citation analysis to information extracted from three areas of
machine learning. We use this information to identify core sets of papers that have
made significant contributions to the fields of hidden Markov models, neural networks
and recurrent neural networks. / AFRIKAANSE OPSOMMING: Ons bied 'n sisteem aan wat gebruik maak van statistiese masjiene wat leer om bibliografiese
inligting uit wetenskaplikke literatuur te identifiseer en ontgin. Tegnieke wat
aangewend word vir die verkenning en insameling van nuttige inligting vanaf die snel
groeiende kennisbron van die WWW, word ondersoek.
Ons gebruik verskuilde Markov modelle vir die herkenning van verwysingsstyl en ontginning
van verwysingsinligting met 'n akuraatheidspeil van to 97%. Hierdie hoë ontginningsakuraatheid
stelons in staat om 'n toepassing van die tegniek op die veld van
masjiene wat leer toe te pas. Ons rapporteer hoe ons die tegnieke gebruik het om literatuur
wat beduidende bydraes in die velde van verskuilde Markov modelle, neurale
netwerke en terugkerende neurale netwerke, te identifiseer.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/49886 |
| Date | 12 1900 |
| Creators | Connan, James |
| Contributors | Omlin, Christian W., Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | English |
| Type | Thesis |
| Format | 63 p. : ill. |
| Rights | Stellenbosch University |
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