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Improving hierarchical multiclass pe...

The University of Texas at Dallas.

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Improving hierarchical multiclass perceptrons for entity detection using Google Sets and pronoun disambiguation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improving hierarchical multiclass perceptrons for entity detection using Google Sets and pronoun disambiguation./
作者:	Quinn, Michael.
面頁冊數:	61 p.
附註:	Adviser: Vincent Ng.
Contained By:	Masters Abstracts International46-05.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1452726
ISBN:	9780549526667

Improving hierarchical multiclass perceptrons for entity detection using Google Sets and pronoun disambiguation.
Quinn, Michael.

Improving hierarchical multiclass perceptrons for entity detection using Google Sets and pronoun disambiguation. - 61 p.

Adviser: Vincent Ng.

Thesis (M.S.C.S.)--The University of Texas at Dallas, 2008.

Entity detection is the process of identifying objects in a body of text and correctly classifying each object into one of a set of predefined types. I propose two extensions to improve the performance of hierarchical multiclass perceptrons when used to solve entity detection. The first extension is to use Google Sets to automatically generate training instances. This will address the problems of too few training instances and high unseen word rates. The second extension is to disambiguate pronouns by identifying the influences on the pronoun classification, and using those influences as features. This will address the problem that pronouns carry less information than nominals and proper nouns. A noticeable improvement in F-measure was seen using the combination of these two approaches.

ISBN: 9780549526667Subjects--Topical Terms:

626642
Computer Science.

Improving hierarchical multiclass perceptrons for entity detection using Google Sets and pronoun disambiguation.
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520 $a Entity detection is the process of identifying objects in a body of text and correctly classifying each object into one of a set of predefined types. I propose two extensions to improve the performance of hierarchical multiclass perceptrons when used to solve entity detection. The first extension is to use Google Sets to automatically generate training instances. This will address the problems of too few training instances and high unseen word rates. The second extension is to disambiguate pronouns by identifying the influences on the pronoun classification, and using those influences as features. This will address the problem that pronouns carry less information than nominals and proper nouns. A noticeable improvement in F-measure was seen using the combination of these two approaches.
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