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Urdu Word Embeddings

Representing words as vectors which encode their semantic properties is an important component in natural language processing. Recent advances in distributional semantics have led to the rise of neural network-based models that use unsupervised learning to represent words as dense, distributed vectors, called `word embeddings'. These embeddings have led to breakthroughs in performance in multiple natural language processing applications, and also hold the key to improving natural language processing for low-resource languages by helping machine learning algorithms learn patterns more easily from these richer representations of words, thereby allowing better generalization from less data. In this paper, we train the skip-gram model on more than 140 million Urdu words to create the first large-scale word embeddings for the Urdu language. We analyze the quality of the learned embeddings by looking at the closest neighbours to different words in the vector space and find that they capture a high degree of syntactic and semantic similarity between words. We evaluate this quantitatively by experimenting with different vector dimensionalities and context window sizes and measuring their performance on Urdu translations of standard word similarity tasks. The embeddings are made freely available in order to advance research on Urdu language processing.