Year of Graduation
Slang Detection and Normalization
Applied Mathematics and Information Science
In this paper we'll suggest an approach on slang detection and normalization in Russian. The topic is most relevant to different resources on the Internet – social networks, forums, online shops. Normalization can be both useful to help moderate web-sites and for specialists in natural language processing while working with various sources of data. The proposed approach is to use Adaptive Skip-Gram neural network to remove the ambiguity of words that are supposed to have at least one non-normative sense. The main reason to choose model is the possibility to create more than one embedding for each word. For selecting a suitable synonym it was decided to use Word2Vec. The two models were trained on a large text corpus that contained different lexicon to make sure that we catch the slang meaning of polysemic words too. The list of slang vocabulary was collected from crowd-sourced resource for Russian teen slang. During the test, the results were evaluated and examples of good and bad substitutions were analyzed with presenting some ways to improve the quality.