The surge of pre-trained language models has begun a new era in the field of Natural
Language Processing (NLP) by allowing us to build powerful language models. Among …

A large volume of unstructured data, especially text data, is generated and exchanged daily.
Consequently, the importance of extracting patterns and discovering knowledge from textual …

Despite the progress made in recent years in addressing natural language understanding
(NLU) challenges, the majority of this progress remains to be concentrated on resource-rich …

This paper discusses the importance of language processing and its challenges. It first
defines low resource languages and their influencing factors. Then talking about the Persian …

Named entity recognition is a natural language processing task to recognize and extract
spans of text associated with named entities and classify them in semantic Categories …

This study investigates to evaluate feasibility of k-means clustering algorithm in order to
improve effectiveness of the results recommended by RICEST Journal Finder System. More …

We introduce FaBERT, a Persian BERT-base model pre-trained on the HmBlogs corpus,
encompassing both informal and formal Persian texts. FaBERT is designed to excel in …

Named Entity Recognition (NER) is one of the essential prerequisites for many natural
language processing tasks. All public corpora for Persian named entity recognition, such as …

Transformer models have had a great impact on natural language processing (NLP) in
recent years by realizing outstanding and efficient contextualized language models. Recent …

The Persian language, also known as Farsi, is distinguished by its intricate morphological
richness, yet it contends with a paucity of linguistic resources. With an estimated 110 million …