Web Science and Digital Libraries Research Group

Introduction: There is a significant amount of research papers and tools for Named-entity recognition (NER); however, only a small potion of it addresses Arabic text and even less tools for extracting named entities from documents written in Arabic. In August 2020, I proposed an approach for  extracting named entities from Arabic text using a combination of tools, Google Translate and Stanford NERC , and produced comparable results to Arabic Linguistic Pipeline (ALP) . The implementation of my approach,  GTS, is available on GitHub . In December 2020, I wrote a  blog post outlining tools and libraries for matching Arabic names written in English , which is important for Entity Linking, a subtask of Natural Language Processing (NLP). While discussing the importance of Entity linking is beyond the scope of this post, merging Arabic named entities written in English is the first step for Entity Linking when processing English documents. This is because discrepancie...

 The Unofficial Google Translate API Enhancing communication between people around the world have become of a special importance over the last decade or so due to the unparalleled growth in globalization, international eCommerce, social networks, internet usage, and other services. Thus, the demand for a tool that is capable of removing language barriers have increased and led to the development of cloud translation services (a.k.a. Translation APIs). There are several translation APIs, but the giant, Google Translate , is what I will be talking about in this post. In 2016, Google translated more than 100 billion words a day; and after 10 years of service, more than 500 million people were using Google Translate . While I am not a translation expert and prefer to use dictionary.com to get an accurate definition of individual words, I have used Google Translate in the past to get a list of synonyms for a word (in English or Arabic) or for quickly translating a long article that I ...