Portuguese Part-of-Speech Tagging Using Entropy Guided Transformation Learning

被引：0

作者：

dos Santos, Cicero Nogueira ^{[1
]}

Milidiu, Ruy L. ^{[1
]}

Renteria, Raul P. ^{[2
]}

机构：

[1] Pontificia Univ Catolica Rio de Janeiro, Dept Informat, Rio De Janeiro, Brazil

[2] Fast Search & Transfer, Oslo, Norway

来源：

COMPUTATIONAL PROCESSING OF THE PORTUGUESE LANGUAGE, PROCEEDINGS | 2008年 / 5190卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Entropy Guided Transformation Learning (ETL) is a new machine learning strategy that combines the advantages of Decision Trees (DT) and Transformation Based Learning (TBL). In this work, we apply the ETL framework to Portuguese Part-of-Speech Taggging. We use two different corpora: Mac-Morpho and Tycho Brahae. ETL achieves the best results reported so far for Machine Learning based POS tagging of both corpora. ETL provides a new training strategy that accelerates transformation learning. For the Mac-Morpho corpus this corresponds to a factor of three speedup. ETL shows accuracies of 96.75% and 96.64% for Mac-Morpho and Tycho Brahae, respectively.

引用

页码：143 / +

页数：3

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