Semantic Annotation to Support Automatic Taxonomy Classification

Sanghee Kim, Saeema Ahmed, Ken Wallace

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


This paper presents a new taxonomy classification method that generates classification criteria from a small number of important sentences identified through semantic annotations, e.g. cause-effect. Rhetorical Structure Theory (RST) is used to discover the semantics (Mann et al. 1988). Specifically, the annotations identify which parts of a text are more important for understanding its contents. The extraction of salient sentences is a major issue in text summarisation. Commonly used methods are based on statistical analysis, but for subject-matter type texts, linguistically motivated natural language processing techniques, like semantic annotations, are preferred. An experiment to test the method using 140 documents collected from industry demonstrated that classification accuracy can be improved by up to 16%.
Original languageEnglish
Title of host publicationDESIGN 2006 : 9th International Design Conference
EditorsMarjanovic Dorian
Number of pages1573
PublisherThe Design Society
Publication date2006
ISBN (Print)953-6313-78-2
Publication statusPublished - 2006
Event9th International Design Conference - Dubrovnik, Croatia
Duration: 15 May 200618 May 2006
Conference number: 9


Conference9th International Design Conference
Internet address

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