Vector representations of text data in deep learning
Table 3.5: Information retrieval results for 32-bit binary codes constructed by first inferring 32d or 300d real-valued paragraph vectors and then employing another unsupervised model or hashing algorithm for binarization. Paragraph vectors were inferred using PV-DBOW with bigrams.
['Binarization model', 'Embed. size', '20 Newsgroups MAP', '20 Newsgroups NDCG@10', 'RCV1 MAP', 'RCV1 NDCG@10', 'EN Wikipedia MAP', 'EN Wikipedia 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.