Contextually Enriched Meta-Learning Ensemble Model for Urdu Sentiment Analysis

Improving Sentiment Analysis Through Ensemble Learning of Meta-level Features

In this research, the well-known microblogging site, Twitter, was used for a sentiment analysis investigation. We propose an ensemble learning approach based on the meta-level features of seven existing lexicon resources for automated polarity sentiment classification. The ensemble employs four base learners (a Two-Class Support Vector Machine, a Two-Class Bayes Point Machine, a Two-Class Logis...

Sentiment analysis: Bayesian Ensemble Learning

Contextually enriched argument linking

In this paper we investigate the role played by one aspect of context, namely encyclopedic knowledge, to the interpretation of clause arguments. We discuss cases where the assignment of a specific semantic role of a predicate to a clause argument is not determined by the grammar alone, but requires extragrammatical resources (in particular, world knowledge), and then present a formally explicit...

Contextually-enriched Documents: Publishing for Organisational

We are looking at how new tools and forms of document interface can be used to support document centered discourse, to enrich documents with contextual information, and ultimately, to support models of organizational learning. Building on the Digital Document Discourse Environment (D3E) we have developed tools to support the publishing of digital documents tightly coupled with discussion spaces...

Ensemble Kernel Learning Model for Prediction of Time Series Based on the Support Vector Regression and Meta Heuristic Search

In this paper, a method for predicting time series is presented. Time series prediction is a process which predicted future system values based on information obtained from past and present data points. Time series prediction models are widely used in various fields of engineering, economics, etc. The main purpose of using different models for time series prediction is to make the forecast with...