Vector representations of text data in deep learning
Table 4.1: Marginal probabilities and nearest neighbors for selected words from the vocabulary. Codes learned with a 3-sense, 300-dimensional Disambiguated Skip-gram model trained with different values of the entropy cost γ. The neighbors are selected are presented only for senses with a marginal probability higher than 0.05. (Part I)
['Word', '[ITALIC] γ', 'Sense 1  [ITALIC] P', 'Sense 1 Nearest neighbors', 'Sense 2  [ITALIC] P', 'Sense 2 Nearest neighbors', 'Sense 3  [ITALIC] P', 'Sense 3 Nearest neighbors']
AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted.