As more and more Arabic texts emerged on the Internet, extracting important information
from these Arabic texts is especially useful. As a fundamental technology, Named entity …

Text clustering is one of the most commonly used methods for detecting themes or types of
documents. Text clustering is used in many fields, but its effectiveness is still not sufficient to …

Named entity recognition (NER) is a subfield of information extraction, which aims to detect
and classify predefined named entities (eg, people, locations, organizations, etc.) in a body …

Recurrent Neural Networks (RNNs) and transformers are deep learning models that have
achieved remarkable success in several Natural Language Processing (NLP) tasks since …

The vast amount of unstructured data spread on a daily basis rises the need for developing
effective information retrieval and extraction methods. Named Entity Recognition is a …

Named Entity Recognition (NER) is one of the fundamental tasks in Natural Language
Processing (NLP), which aims to locate, extract, and classify named entities into a …

Named entity recognition (NER) plays a significant role in many applications such as
information extraction, information retrieval, question answering, and even machine …

Deep learning methods have been applied to Chinese named entity recognition for the
online medical consultation. They require a large number of marked samples. However, no …

Accepted: 7 February 2020 In the last few years, significant amounts of text data have
emerged on the different social media platforms. A tendency to extract valuable information …

Named entity recognition (NER) towards product review intends to identify domain
dependent named entities (eg, organisation name, product name, etc.) from product reviews …