TY - CHAP

T1 - Domain adaptation of POS taggers without handcrafted features

AU - Rodrigues, Irving M.

AU - Fernandes, Eraldo R.

AU - dos Santos, Cicero N.

PY - 2017/6/30

Y1 - 2017/6/30

N2 - Unsupervised domain adaptation is an attractive option when labeled data is lacking for some domain of interest but is available for other domain. Part-of-speech (POS) tagging is often considered a solved task when enough labeled data is available in the domain of interest. However, when considering a domain adaptation scenario, this is far from true. Several approaches have been proposed for domain adaptation of POS taggers, however as far as we know, all of them are based on handcrafted features. In this work, we employ a machine learning method whose input is exclusively composed of the raw text. This method learns word- and character-level representations (embeddings), and has been successfully applied to intra-domain tasks. We show that this method achieves strong performances on the domain adaptation of English and Portuguese POS taggers.

AB - Unsupervised domain adaptation is an attractive option when labeled data is lacking for some domain of interest but is available for other domain. Part-of-speech (POS) tagging is often considered a solved task when enough labeled data is available in the domain of interest. However, when considering a domain adaptation scenario, this is far from true. Several approaches have been proposed for domain adaptation of POS taggers, however as far as we know, all of them are based on handcrafted features. In this work, we employ a machine learning method whose input is exclusively composed of the raw text. This method learns word- and character-level representations (embeddings), and has been successfully applied to intra-domain tasks. We show that this method achieves strong performances on the domain adaptation of English and Portuguese POS taggers.

KW - Informatics

KW - tagging

KW - syntactics

KW - training

KW - Vocabulary

KW - training data

KW - Feature extraction

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85030974151&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2017.7966274

DO - 10.1109/IJCNN.2017.7966274

M3 - Article in conference proceedings

AN - SCOPUS:85030974151

SN - 978-1-5090-6183-9

T3 - Proceedings of the International Joint Conference on Neural Networks

SP - 3331

EP - 3338

BT - IJCNN 2017

PB - Institute of Electrical and Electronics Engineers Inc.

CY - Piscataway

T2 - International Joint Conference on Neural Networks

Y2 - 14 May 2017 through 19 May 2017

ER -