A Precisely Xtreme-Multi Channel Hybrid Approach For Roman Urdu Sentiment Analysis

no code implementations • 11 Mar 2020 • Faiza Memood, Muhammad Usman Ghani, Muhammad Ali Ibrahim, Rehab Shehzadi, Muhammad Nabeel Asim

In order to accelerate the performance of various Natural Language Processing tasks for Roman Urdu, this paper for the very first time provides 3 neural word embeddings prepared using most widely used approaches namely Word2vec, FastText, and Glove.

Sentiment Analysis Word Embeddings

Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification

no code implementations • 3 Mar 2020 • Muhammad Nabeel Asim, Muhammad Usman Ghani, Muhammad Ali Ibrahim, Sheraz Ahmad, Waqar Mahmood, Andreas Dengel

Second, it investigates the performance impact of traditional machine learning based Urdu text document classification methodologies by embedding 10 filter-based feature selection algorithms which have been widely used for other languages.

Automated Feature Engineering BIG-bench Machine Learning +6