textual | Shahabuddin Amerudin @ UTM

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

By Dr. 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, explore its diverse applications, and focus on a specific use case: geospatial data extraction facilitated by the EntityRecognizer model. The Mechanism Behind NER At its […]

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

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 more. This computational feat yields valuable insights from extensive textual data and finds its application across a plethora of scenarios. The Mechanism Behind NER NER