Polyglot: Distributed Word Representations for Multilingual NLP

This work quantitatively demonstrates the utility of word embeddings by using them as the sole features for training a part of speech tagger for a subset of these languages and investigates the semantic features captured through the proximity of word groupings.

Abstract

Distributed word representations (word embeddings) have recently contributed to competitive performance in language modeling and several NLP tasks. In this work, we train word embeddings for more than 100 languages using their corresponding Wikipedias. We quantitatively demonstrate the utility of our word embeddings by using them as the sole features for training a part of speech tagger for a subset of these languages. We find their performance to be competitive with near state-of-art methods in English, Danish and Swedish. Moreover, we investigate the semantic features captured by these embeddings through the proximity of word groupings. We will release these embeddings publicly to help researchers in the development and enhancement of multilingual applications.