# Co-occurrences using Fasttext embeddings for word similarity tasks in Urdu

arXiv: https://arxiv.org/abs/2102.10957 · HTML: https://mlsanity.com/paper/2102.10957/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 58 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation](https://mlsanity.com/paper/1408.3456/) (2014)
- [Correlation Coefficients and Semantic Textual Similarity](https://mlsanity.com/paper/1905.07790/) (2019)
- [An Improved Text Sentiment Classification Model Using TF-IDF and Next Word Negation](https://mlsanity.com/paper/1806.06407/) (2018)

## Abstract (arXiv)

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.