The Multidisciplinary Facets of Research on Humour

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

The Role of Non-Ambiguous Words in Natural Language Disambiguation

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

[Review] The Text Mining Handbook: Advanced Approaches to Analyzing Unstructured Data

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

Co-training and Self-training for Word Sense Disambiguation

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

Classifier Stacking and Voting for Text Filtering

This article discusses classifier stacking and voting for text filtering

Using Wikipedia for Automatic Word Sense Disambiguation

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

SenseLearner: Minimally Supervised Word Sense Disambiguation for All Words in Open Text

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%

Open Text Semantic Parsing Using FrameNet and WordNet

This article discusses open text semantic parsing using FrameNet and WordNet

Putting Pieces Together: Combining FrameNet, VerbNet and WordNet for Robust Semantic Parsing

This paper describes the authors' work in integrating three different lexical resources: FrameNet, VerbNet, and WordNet, into a unified, richer knowledge-base, to the end of enabling more robust semantic parsing

Learning to Identify Educational Materials

This paper discusses learning to identify educational materials