Robust Word Vectors: Context-Informed Embeddings for Noisy Texts
Valentin Andreevich Malykh, Varvara Logacheva, Taras Khakhulin · 2018
We suggest a new language-independent architecture of robust word vectors (RoVe).It is designed to alleviate the issue of typos, which are common in almost any user-generated content, and hinder automatic text processing.Our model is morphologically motivated, which allows it to deal with unseen word forms in morphologically rich languages.We present the results on a number of Natural Language Processing (NLP) tasks and languages for the variety of related architectures and show that proposed architecture is typo-proof.