NLP | Shahabuddin Amerudin @ UTM

Navigating the Future: Autonomous Driving and the Crucial Technologies Behind It

Shah / September 13, 2023 / Knowledge, Public, Research / autonomous, driving, navigation, NLP, vehicle, vision / 4 minutes of reading

Introduction The concept of autonomous driving, once relegated to science fiction, is rapidly becoming a reality in the automotive industry. This transformative technology has the potential to reshape our daily lives, making transportation safer, more efficient, and environmentally friendly. In this article, we’ll delve into the world of autonomous driving and explore the various technologies required to make it a viable and reliable mode of transportation. The Vision-Based Foundation At the heart of autonomous driving […]

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The Dynamic Potential of Named Entity Recognition (NER) in Extracting and Analyzing Geospatial Data

Shah / August 20, 2023 / Knowledge, Public, Research, Teaching / AI, Geospatial, GIS, ML, NER, NLP, textual / 3 minutes of reading

By Shahabuddin Amerudin Named Entity Recognition (NER), an integral component of Natural Language Processing (NLP), plays a pivotal role in extracting meaningful information from unstructured text. This technique involves the identification and classification of specific entities within text, ranging from names of people and organizations to temporal expressions and geographic locations. The applications of NER are wide-ranging and impactful across diverse industries. In this comprehensive article, we will delve deeper into the mechanics of NER,

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Unlocking Textual Insights: The Power and Applications of Named Entity Recognition (NER)

Shah / August 20, 2023 / Knowledge, Public, Research, Teaching / AI, ML, NER, NLP, textual / 3 minutes of reading

By Shahabuddin Amerudin Named Entity Recognition (NER), often referred to as entity chunking, extraction, or identification, is a vital process in the realm of Natural Language Processing (NLP). It revolves around the identification and classification of crucial information, known as entities, within text. These entities can be single words or phrases consistently referring to the same concept. Through NER, we can automatically categorize these entities into predetermined classes, such as “Person,” “Organization,” “Time,” “Location,” and

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