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A general purpose semantic parser us...

Shi, Lei.

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A general purpose semantic parser using FrameNet and WordNet.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A general purpose semantic parser using FrameNet and WordNet./
作者:	Shi, Lei.
面頁冊數:	57 p.
附註:	Source: Masters Abstracts International, Volume: 42-06, page: 2245.
Contained By:	Masters Abstracts International42-06.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1419753
ISBN:	0496245056

A general purpose semantic parser using FrameNet and WordNet.
Shi, Lei.

A general purpose semantic parser using FrameNet and WordNet. - 57 p.

Source: Masters Abstracts International, Volume: 42-06, page: 2245.

Thesis (M.S.)--University of North Texas, 2004.

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.

ISBN: 0496245056Subjects--Topical Terms:

626642
Computer Science.

A general purpose semantic parser using FrameNet and WordNet.
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245 1 2 $a A general purpose semantic parser using FrameNet and WordNet.
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500 $a Source: Masters Abstracts International, Volume: 42-06, page: 2245.
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502 $a Thesis (M.S.)--University of North Texas, 2004.
520 $a 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.
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