Intent Detection in Urdu Queries using Fine-tuned BERT models

Shams, Sana; Sadia, Bareera; Aslam, Muhammad

doi:10.1109/icosst57195.2022.10016834

Cited by 4 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Urdu text document classification was performed using DL models in comparison with ML models in product manufacturing data [43]. The intent detection of users data in Urdu was performed after information retrieval using Bidirectional Encoder Representation from Transformers (BERT) [44]. To detect threatening content in the Urdu language, a stacking model was developed using Naïve Bayes and was applied for learning, while Logistic Regression was used for meta-learning; the stacked model compared well with other approaches [45].…”

Section: Relation To Previous Workmentioning

confidence: 99%

Innovations in Urdu Sentiment Analysis Using Machine and Deep Learning Techniques for Two-Class Classification of Symmetric Datasets

Muhammad

Burney

2023

Symmetry

View full text Add to dashboard Cite

Many investigations have performed sentiment analysis to gauge public opinions in various languages, including English, French, Chinese, and others. The most spoken language in South Asia is Urdu. However, less work has been carried out on Urdu, as Roman Urdu is also used in social media (Urdu written in English alphabets); therefore, it is easy to use it in English language processing software. Lots of data in Urdu, as well as in Roman Urdu, are posted on social media sites such as Instagram, Twitter, Facebook, etc. This research focused on the collection of pure Urdu Language data and the preprocessing of the data, applying feature extraction, and innovative methods to perform sentiment analysis. After reviewing previous efforts, machine learning and deep learning algorithms were applied to the data. The obtained results were compared, and hybrid methods were also recommended in this research, enabling new avenues to conduct Urdu language data sentiment analysis.

show abstract

Section: Relation To Previous Workmentioning

confidence: 99%