![]() SpaCy is a popular Natural Language Processing library that can be used for named entity recognition and number of other NLP tasks. Named entities can be a person, organization, location, date, time, or even quantity. Named entity recognition (NER) is a task that is concerned with identifying and classifying named entities in textual data. Let's take a look at a few natural language processing techniques for extracting information from unstructured text: As the machine learning technology continues to develop, we will only see more and more information extraction use cases covered. Industries such as healthcare, finance, and ecommerce are already using natural language processing techniques to extract information and improve business processes. With the help of natural language processing, computers can make sense of the vast amount of unstructured text data that is generated every day, and humans can reap the benefits of having this information readily available. ![]() These techniques can be used to extract information such as entity names, locations, quantities, and more. There are a number of natural language processing techniques that can be used to extract information from text or unstructured data, and in this blog post we will explore a few of them. This is particularly relevant in the realm of natural language processing (NLP), where machines are tasked with making sense of unstructured text data. As the field of artificial intelligence advances, so does the capability of machine learning to interpret and extract information from human language.
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