A General Purpose Semantic Parser Using FrameNet and WordNet®.

Syntactic parsing is one of the best understood language processing applications. Since language and grammar have been formally defined, it is easy for computers to parse the syntactic structure of natural language text. Does meaning have structure as well? If it has, how can we analyze the structure? Previous systems rely on a one-to-one correspondence between syntactic rules and semantic rules. But such systems can only be applied to limited fragments of English. In this thesis, we propose a general-purpose shallow semantic parser which utilizes a semantic network (WordNet), and a frame dataset (FrameNet). Semantic relations recognized by the parser are based on how human beings represent knowledge of the world. Parsing semantic structure allows semantic units and constituents to be accessed and processed in a more meaningful way than syntactic parsing, moving the automation of understanding natural language text to a higher level.

Grantor dc:publisher

University of North Texas

Year dc:date

2004

Author dc:creator

• Shi, Lei

Contributors dc:contributor

• Mihalcea, Rada, 1974-
• Brazile, Robert

• Semantic networks (Information theory)
• WordNet.
• FrameNet.
• Natural language processing (Computer science)
• Parsing (Computer grammar)
• syntactic parsing
• semantic parser
• WordNet
• FrameNet

Statement dc:rights

• Public
• Copyright
• Shi, Lei
• Copyright is held by the author, unless otherwise noted. All rights reserved.

Language dc:language

English

Identifier

oclc: 55874199
https://digital.library.unt.edu/ark:/67531/metadc4483/
ark: ark:/67531/metadc4483

OAI identifier oai:identifier

info:ark/67531/metadc4483