Co-occurrences using Fasttext embeddings for word similarity tasks in Urdu
This work addresses the problem of under-resourced languages like Urdu for NLP researchers, but it is incremental as it applies existing methods to new data.
The authors tackled the lack of efficient language models for Urdu by building a corpus and modifying Fasttext and N-Gram models to train word embeddings, then used these for a word similarity task and compared results with existing techniques.
Urdu is a widely spoken language in South Asia. Though immoderate literature exists for the Urdu language still the data isn't enough to naturally process the language by NLP techniques. Very efficient language models exist for the English language, a high resource language, but Urdu and other under-resourced languages have been neglected for a long time. To create efficient language models for these languages we must have good word embedding models. For Urdu, we can only find word embeddings trained and developed using the skip-gram model. In this paper, we have built a corpus for Urdu by scraping and integrating data from various sources and compiled a vocabulary for the Urdu language. We also modify fasttext embeddings and N-Grams models to enable training them on our built corpus. We have used these trained embeddings for a word similarity task and compared the results with existing techniques.