Final Activity Report Summary - UNFEX (Utilising and enhancing unsupervised feature extraction)
During my fellowship I developed and analysed new model selection methods for unsupervised feature extraction like nonlinear dimensionality reduction (NLDR). Those are techniques which are able to find simple descriptions of high-dimensional data, as they appear in all branches of science and industry. The results of my work can help practitioners to assess the quality of results obtained from state-of-the-art NLDR algorithms.
Furthermore, I proposed and evaluated a new approach to the problem of textual entailment which tries to automatically answer the question whether one piece of text entails some other piece of text. For this, I combined ideas from logic and probability to obtain a calculus that tries to mimic human reasoning (which is often only approximately sound).
Furthermore, I proposed and evaluated a new approach to the problem of textual entailment which tries to automatically answer the question whether one piece of text entails some other piece of text. For this, I combined ideas from logic and probability to obtain a calculus that tries to mimic human reasoning (which is often only approximately sound).