TY - CHAP

T1 - A machine learning approach to Portuguese clause identification

AU - Fernandes, Eraldo R.

AU - Dos Santos, Cícero N.

AU - Milidiú, Ruy L.

N1 - Conference code: 9

PY - 2010

Y1 - 2010

N2 - In this work, we apply and evaluate a machine-learningbased system to Portuguese clause identification. To the best of our knowledge, this is the first machine-learning-based approach to this task. The proposed system is based on Entropy Guided Transformation Learning. In order to train and evaluate the proposed system, we derive a clause annotated corpus from the Bosque corpus of the Floresta Sint́a(c)tica Project - an European and Brazilian Portuguese treebank. We include part-of-speech (POS) tags to the derived corpus by using an automatic state-of-the-art tagger. Additionally, we use a simple heuristic to derive a phrase-chunk-like (PCL) feature from phrases in the Bosque corpus. We train an extractor to this sub-task and use it to automatically include the PCL feature in the derived clause corpus. We use POS and PCL tags as input features in the proposed clause identifier. This system achieves a Fβ=1 of 73.90, when using the golden values of the PCL feature. When the automatic values are used, the system obtains Fβ=1 = 69.31. These are promising results for a first machine learning approach to Portuguese clause identification. Moreover, these results are achieved using a very simple PCL feature, which is generated by a PCL extractor developed with very little modeling effort.

AB - In this work, we apply and evaluate a machine-learningbased system to Portuguese clause identification. To the best of our knowledge, this is the first machine-learning-based approach to this task. The proposed system is based on Entropy Guided Transformation Learning. In order to train and evaluate the proposed system, we derive a clause annotated corpus from the Bosque corpus of the Floresta Sint́a(c)tica Project - an European and Brazilian Portuguese treebank. We include part-of-speech (POS) tags to the derived corpus by using an automatic state-of-the-art tagger. Additionally, we use a simple heuristic to derive a phrase-chunk-like (PCL) feature from phrases in the Bosque corpus. We train an extractor to this sub-task and use it to automatically include the PCL feature in the derived clause corpus. We use POS and PCL tags as input features in the proposed clause identifier. This system achieves a Fβ=1 of 73.90, when using the golden values of the PCL feature. When the automatic values are used, the system obtains Fβ=1 = 69.31. These are promising results for a first machine learning approach to Portuguese clause identification. Moreover, these results are achieved using a very simple PCL feature, which is generated by a PCL extractor developed with very little modeling effort.

KW - Informatics

KW - Machine Learn Approach

KW - Training Corpus

KW - Shared Task

KW - Human Language Technology

KW - Corpus Format

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=78650284958&partnerID=8YFLogxK

UR - https://d-nb.info/1000569276

U2 - 10.1007/978-3-642-12320-7_8

DO - 10.1007/978-3-642-12320-7_8

M3 - Article in conference proceedings

AN - SCOPUS:78650284958

SN - 3-642-12319-8

SN - 978-3-642-12319-1

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 55

EP - 64

BT - Computational Processing of the Portuguese Language

A2 - Pardo, Thiago Alexandre Salgueiro

A2 - Branco, Antonio

A2 - Klautau, Aldebaro

A2 - Viera, Renata

A2 - de Lima, Vera Lucia Strube

PB - Springer

CY - Berlin, Heidelberg

T2 - International Conference on Computational Processing of the Portuguese Language

Y2 - 27 April 2010 through 30 April 2010

ER -