Vector representations of text data in deep learning
Table 3.2: Information retrieval RCV1 results. The best binary results for each code dimensionality are highlighted.
['Code', 'Model', 'Include', 'MAP', 'NDCG@10']
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. Results for both Paragraph Vector and Binary Paragraph Vector for different code sizes are reported in Tab.