EXTENSION OF CONVENTIONAL CO-TRAINING LEARNING STRATEGIES TO THREE-VIEW AND COMMITTEE-BASED LEARNING STRATEGIES FOR EFFECTIVE AUTOMATIC SENTENCE SEGMENTATION
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The objective of this work is to develop effective multi-view semi-supervised machine learning strategies for sentence boundary classification problem when only small sets of sentence boundary labeled data are available. We propose three-view and committee-based learning strategies incorporating with co-training algorithms with agreement, disagreement, and self-combined learning strategies using prosodic, lexical and morphological information. We compare experimental results of proposed three-view and committee-based learning strategies to other semi-supervised learning strategies in the literature namely, self-training and co-training with agreement, disagreement, and self-combined strategies. The experiment results show that sentence segmentation performance can be highly improved using multi-view learning strategies that we propose since data sets can be represented by three redundantly sufficient and disjoint feature sets. We show that the proposed strategies substantially improve the average performance when only a small set of manually labeled data is available for Turkish and English spoken languages, respectively.
Açıklama
IEEE Workshop on Spoken Language Technology (SLT) -- DEC 18-21, 2018 -- Athens, GREECE
Anahtar Kelimeler
Boosting, Co-Training, Sentence Segmentation, Semi-supervised learning, Prosody
Kaynak
2018 Ieee Workshop On Spoken Language Technology (Slt 2018)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
