Web Science and Digital Libraries Research Group

  Introduction: The increased use of Natural Language Processing (NLP) techniques is fueled by the need to process massive amounts of data, the demand for clever chat bots, and other human-computer interaction tasks. Named Entity Recognition (NER) is one of the most important techniques in NLP . The extracted named entities offer computers a way to classify documents, perform semantic analysis on textual information, etc.  In other words, NLP allows machines to understand human language(s). Speaking of languages, Cross-Language Information Retrieval (CLIR) gained traction in the past two decades or so due to the unprecedented rise in globalization, transnational companies, international news outlets, social media, and internet use. CLIR requires a translation service since CLIR deals with retrieving information written in languages different from the language of the user's query. In August 2020, I proposed an approach for  extracting named entities from Arabic tex...

  Natural Language Processing (NLP): The recent advancement in Natural Language Processing (NLP) has allowed machines to process massive amounts of data found on the internet and elsewhere. The data revolution isn’t only about numbers because, in addition to numbers, data include words, images, videos, etc. Therefore, researchers are working on teaching machines how to process natural languages to interact with humans, summarize data, extract information, etc. The fact that machines now have to interpret human languages opens the door for new opportunities for NLP software that facilitate interactions between humans and computers. Named Entity Recognition and Classification (NERC) is one of the most important techniques in NLP . Names of persons, locations, and organizations extracted from a document enable computers to understand the content of the document. Cross-Language Information Retrieval (CLIR): The importance of Cross-language information retrieval (CLIR) comes from t...