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Yayın Co-training using prosodic, lexical and morphological information for automatic sentence segmentation of Turkish spoken language(Işık Üniversitesi, 2018-01-15) Dalva, Doğan; Güz, Ümit; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Elektronik Mühendisliği Doktora ProgramıSentence segmentation of speech aims detecting sentence boundaries in a stream of words output by the speech recognizer. Sentence segmentation is a preliminary step toward speech understanding. It is of particular importance for speech related applications, as most of the further processing steps; such as parsing, machine translation and information extraction, assume the presence of sentence boundaries. Typically, statistical methods require a huge amount of manually labeled data, which is time and labor consuming process to prepare. In this work, novel multiview semi-supervised learning strategies for the solution of sentence segmentation problem are proposed. The aim of this work is to and effective semi-supervised machine learning strategies when only a small set of sentence boundary labeled data is available. This work proposes three-view co-training and committee-based strategies incorporating with agreement, disagreement and self-combined strategies using lexical, morphological and prosodic information, and investigates performance of the proposed learning strategies against baseline, self-training and co-training. The experimental results show that the proposed learning strategies highly improve the sentence segmentation problem, since data sets can be represented by three redundantly suffcient and disjoint feature sets.
