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Found 5 papers, 2 papers with code
Quantitative knowledge retrieval from large language models
Large language models (LLMs) have been extensively studied for their abilities to generate convincing natural language sequences, however their utility for quantitative information retrieval is less well understood.
A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content
Following the need for an optimal language model competent in extracting useful patterns from social media text, the key goal of this paper is to train language models in such a way that they learn to derive generalized patterns.
A Precisely Xtreme-Multi Channel Hybrid Approach For Roman Urdu Sentiment Analysis
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.
Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification
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.
A Robust Hybrid Approach for Textual Document Classification
Evaluation results reveal that the proposed methodology outperforms the state-of-the-art of both the (traditional) machine learning and deep learning based text document classification methodologies with a significant margin of 7. 7% on 20 Newsgroups and 6. 6% on BBC news datasets.
