Minimally-Supervised Morphological Segmentation using Adaptor Grammars

This paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semi-supervised training, and a novel model selection method. In the model selection method, we train unsupervised Adaptor Grammars using an over-articulated metagrammar, then use a small labelled data set to select which potential morph boundaries identified by the metagrammar should be returned in the final output. We evaluate on five languages and show that semi-supervised training provides a boost over unsupervised training, while the model selection method yields the best average results over all languages and is competitive with state-of-the-art semi-supervised systems. Moreover, this method provides the potential to tune performance according to different evaluation metrics or downstream tasks.

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A Model Selection Method for S-Estimation

SSRN Electronic Journal ◽

10.2139/ssrn.885926 ◽

2005 ◽

Cited By ~ 1

Author(s):

Arie Preminger ◽

Shinichi Sakata

Keyword(s):

Model Selection ◽

Selection Method ◽

Model Selection Method

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Pedestrian detection algorithm in traffic scene based on weakly supervised hierarchical deep model

International Journal of Advanced Robotic Systems ◽

10.1177/1729881417692311 ◽

2016 ◽

Vol 14 (1) ◽

pp. 172988141769231 ◽

Cited By ~ 2

Author(s):

Yingfeng Cai ◽

Youguo He ◽

Hai Wang ◽

Xiaoqiang Sun ◽

Long Chen ◽

...

Keyword(s):

Deep Learning ◽

Pedestrian Detection ◽

Recognition Rate ◽

Detection Algorithm ◽

Training Methods ◽

Two Dimensional ◽

Data Set ◽

Deep Model ◽

Unsupervised Training ◽

Weakly Supervised

The emergence and development of deep learning theory in machine learning field provide new method for visual-based pedestrian recognition technology. To achieve better performance in this application, an improved weakly supervised hierarchical deep learning pedestrian recognition algorithm with two-dimensional deep belief networks is proposed. The improvements are made by taking into consideration the weaknesses of structure and training methods of existing classifiers. First, traditional one-dimensional deep belief network is expanded to two-dimensional that allows image matrix to be loaded directly to preserve more information of a sample space. Then, a determination regularization term with small weight is added to the traditional unsupervised training objective function. By this modification, original unsupervised training is transformed to weakly supervised training. Subsequently, that gives the extracted features discrimination ability. Multiple sets of comparative experiments show that the performance of the proposed algorithm is better than other deep learning algorithms in recognition rate and outperforms most of the existing state-of-the-art methods in non-occlusion pedestrian data set while performs fair in weakly and heavily occlusion data set.

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