Automatic lexico-semantic acquisition for question answering

"Freedom" and "liberty" share the same meaning. "Paris" denotes a city, and the word "party" triggers associations of "wine" and "fun" for many. People naturally acquire these lexico-semantic relations such as synonyms, categorised named entities, and associations by using language in their daily life.

For many natural language processing applications, such as question answering, this type of information is essential, e.g. to recognise that a particular meaning can be inferred from different text variants or to compensate for the lack of general world knowledge.

This thesis proposes three methods for using large text corpora to acquire lexico-semantic information automatically: a syntax-based method, a multilingual word-alignment-based method and a proximity-based method. The three methods complement each other in the type of data needed, the way they deal with sparse data and most importantly, in the types of lexico-semantic information they provide. This information is then applied to a question answering system. Among the different types of lexico-semantic information acquired, categorised named entities, e.g. "Paris" denotes a city, improved the system the most and this information was obtained with the syntax-based method.

	Titel en inhoudsopgave
	Hoofdstuk 1
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	Conclusies
	Unfinished sympathies
	Bibliografie
	Nederlandse samenvatting
	GRODIL
	Volledige dissertatie

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