Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning

Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning

Morpho-syntactic lexicons provide information about the morphological and syntactic roles of words in a language. Such lexicons are not available for all languages and even when available, their coverage can be limited. We present a graph-based semi-supervised learning method that uses the morphological, syntactic and semantic relations between words to automatically construct wide coverage lex...

Graph-Based Semi-Supervised Learning

While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in ...

Parallel Graph-Based Semi-Supervised Learning

Semi-supervised learning (SSL) is the process of training decision functions using small amounts of labeled and relatively large amounts of unlabeled data. In many applications, annotating training data is time-consuming and error prone. Speech recognition is the typical example, which requires large amounts of meticulously annotated speech data (Evermann et al., 2005) to produce an accurate sy...

Graph Based Multi-class Semi-supervised Learning Using Gaussian Process

This paper proposes a multi-class semi-supervised learning algorithm of the graph based method. We make use of the Bayesian framework of Gaussian process to solve this problem. We propose the prior based on the normalized graph Laplacian, and introduce a new likelihood based on softmax function model. Both the transductive and inductive problems are regarded as MAP (Maximum A Posterior) problem...

Hyper-parameter Learning for Graph Based Semi-supervised Learning Algorithms