The vast majority of the existing approaches for taxonomy enrichment apply word embeddings as they have proven to accumulate contexts (in a broad sense) extracted from texts which are sufficient for attaching orphan words to the taxonomy.On the other hand, apart from being large lexical and semantic resources, taxonomies are graph structures.Combining word embeddings with graph structure of taxonomy could be of use for predicting taxonomic relations.In this paper we compare several approaches for attaching new words to the existing taxonomy which are based on the graph representations with the one that relies on fastText embeddings.We test all methods on Russian and English datasets, but they could be also applied to other wordnets and languages.

Nowadays, misleading information spreads over the internet at an incredible speed, which can lead to irreparable consequences. As a result, it is becoming more and more essential to combat fake news, especially in the early stages of its origins. Over the past years, a lot of work has been done in this direction. However, all existed solutions have their limitations. One of the main limitations of the current approaches is that the majority of the models are focused only on one language and do not use any multilingual information. In this work, we investigate the new approach of fake news detection based on multilingual evidence. We show effectiveness of the proposed approach in a manual and an automated evaluation experiments.

\beginabstract We analyze comparative questions, i.e., questions asking to compare different items, that were submitted to Yandex in 2012. Responses to such questions might be quite different from the simple "ten blue links'' and could, for example, aggregate pros and cons of the different options as direct answers. However, changing the result presentation is an intricate decision such that the classification of comparative questions forms a highly precision-oriented task.

We present CAM (comparative argumentative machine), a novel open-domain IR system to argumentatively compare objects with respect to information extracted from the Common Crawl. In a user study, the participants obtained 15% more accurate answers using CAM compared to a "traditional" keyword-based search and were 20% faster in finding the answer to comparative questions.