Vector representations of text data in deep learning
Table 3.7: Information retrieval results for the Real-Binary PV-DBOW model. Real valued representations have 300 dimensions. (A) Binary codes are used for selecting documents within a given Hamming distance to the query and real-valued representations are used for ranking. (B) For comparison, variant A was repeated with binary codes inferred using plain Binary PV-DBOW and a real-valued representation inferred using original PV-DBOW model.
['Code size', 'Radius', 'NDCG@10 20 Newsgroups', 'NDCG@10 20 Newsgroups', 'NDCG@10 RCV1', 'NDCG@10 RCV1', 'NDCG@10 English Wikipedia', 'NDCG@10 English Wikipedia']
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. We evaluated the Real-Binary PV-DBOW using the same text datasets as in the case of Binary PV-DBOW, namely 20 Newsgroups, RCV1 and English Wikipedia. The only metric that we report in this evaluation is NDCG@10. We do not report MAP, because MAP is estimated using the whole recall range and the inherent trait of the filtering approach is that high recall levels may not be accessible. In Tab.