This paper investigates the application of co-training and self-training to word sense disambiguation. Optimal and empirical parameter selection methods for co-training and self-training are investigated, with various degrees of error reduction. A new method that combines co-training with majority voting is introduced, with the effect of smoothing the bootstrapping learning curves, and improving the average performance.

Abstract: This paper presents an innovative unsupervised method for automatic sentence extraction using graph-based ranking algorithms. We evaluate the method in the context of a text summarization task, and show that the results obtained compare favorably with previously published results on established benchmarks.

This paper discusses instance based learning with automatic feature selection applied to word sense disambiguation.

This paper discusses language independent extractive summarization.

This paper discusses the main lines of research in deriving efficient Word Sense Disambiguation.

In this paper, the authors summarize the main theories of humor that emerged from philosophical and modern psychological research, and survey the past and present developments in the fields of theoretical and computational linguistics.

This article discusses performance analysis of a part of speech tagging task.

This article reviews the book "'The Text Mining Handbook: Advanced Approaches to Analyzing Unstructured Data," by Ronen Feldman and James Sanger.

This article discusses the role of non-ambiguous words in natural language disambiguation.

This paper introduces the semantic wildcard, one of the most powerful operators implemented in IRSLO, which allows for searches along general-specific lines.

This paper discusses a semi-complete disambiguation algorithm for open text.

This paper introduces a graph-based algorithm for sequence data labeling, using random walks on graphs encoding label dependencies. The algorithm is illustrated and tested in the context of an unsupervised word sense disambiguation problem, and shown to significantly outperform the accuracy achieved through individual label assignment, as measured on standard sense-annotated data sets.

This paper describes a method for generating sense-tagged data using Wikipedia as a source of sense annotations. Through word sense disambiguation experiments, the authors show that the Wikipedia-based sense annotations are reliable and can be used to construct accurate sense classifiers.

Article discussing word sense disambiguation with pattern learning and automatic feature selection.

This paper discusses creating annotated data collections.

This paper describes SenseLearner, a minimally supervised word sense disambiguation system that attempts to disambiguate all content words in a text using WordNet senses.

This paper introduces the use of Wikipedia as a resource for automatic keyword extraction and word sense disambiguation, and shows how this online encyclopedia can be used to achieve state-of-the-art results on both these tasks.

This paper introduces SenseLearner - a minimally supervised sense tagger that attempts to disambiguate all content words in a text using the sense from WordNet. SenseLearner participated in the SENSEVAL-3 English all words task, and achieved an average accuracy of 64.6%.

This article discusses using the essence of texts to improve document classification.

This article discusses communicating simple sentences using pictorial representations.

This paper discusses automatic generation of a coarse grained WordNet.

This article discusses document indexing using named entities.

This paper discusses eXtended WordNet.

This paper discusses an iterative approach to Word Sense Disambiguation.