Initial Normalization of User Generated Content: Case Study in a Multilingual Setting

Abstract

We address the problem of normalizing user generated content in a multilingual setting. Specifically, we target comment sections of popular Kazakhstani Internet news outlets, where comments almost always appear in Kazakh or Russian, or in a mixture of both. Moreover, such comments are noisy, i.e. difficult to process due to (mostly) intentional breach of spelling conventions, which aggravates data sparseness problem. Therefore, we propose a simple yet effective normalization method that accounts for multilingual input. We evaluate our approach extrinsically, on the tasks of language identification and sentiment analysis, showing that in both cases normalization improves overall accuracy.