Learn More
Despite the simplicity of the Naive Bayes classifier, it has continued to perform well against more sophisticated newcomers and has remained, therefore, of great interest to the machine learning community. Of numerous approaches to refining the naive Bayes classifier, attribute weighting has received less attention than it warrants. Most approaches, perhaps(More)
The amount of information available on the world wide web keeps growing at an exponential pace. Social tagging is a feature of various online social networks to organize information elements by letting people label these with free-form text, called tags. The graph created by this process is often called a folksonomy and comprises the association between(More)
Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing text. Beginning with an approach that used speech-based features, sarcasm detection has witnessed great interest from the sentiment analysis community. This paper is the(More)
The aim of query-based sampling is to obtain a sufficient, representative sample of an underlying (text) collection. Current measures for assessing sample quality are too coarse grain to be informative. This paper outlines a measure of finer granularity based on probabilistic topic models of text. The assumption we make is that a representative sample(More)
This paper makes a simple increment to state-of-the-art in sarcasm detection research. Existing approaches are unable to capture subtle forms of context incongruity which lies at the heart of sarcasm. We explore if prior work can be enhanced using semantic similarity/discordance between word embed-dings. We augment word embedding-based features to four(More)
This paper is a novel study that views sarcasm detection in dialogue as a sequence labeling task, where a dialogue is made up of a sequence of utterances. We create a manually-labeled dataset of dialogue from TV series 'Friends' annotated with sarcasm. Our goal is to predict sarcasm in each utterance, using sequential nature of a scene. We show performance(More)
We are working on a system for the optimised access and replication of data on a Data Grid. Our approach is based on the use of an economic model that includes the actors and the resources in the Grid. Optimisation is obtained via interaction of the actors in the model, whose goals are maximising the profits and minimising the costs of data resource(More)