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An NLP Movie HistoryPlotting the ebbs and flows of cinema trends The Liberty Theater in New Orleans, Louisiana, ca. 1936. From The New York Public Library Are superhero films going to fall out of fashion? Marvel fans might feel skeptical about ever seeing the likes of Black Widow or Thor bomb at the box office—and their attitude is certainly justified by the overwhelming success of movies like Spider-Man: No Way Home or The Batman. Still, cinema history is full of twists and turns: genres that have risen to popularity, remained in the spotlight for a while, and then quietly left the stage. Think, for instance, of the witty screwball comedies of the 1930s, the intricate noirs of the 1940s, or the sensational disaster movies of the 1970s.Can data science help us make sense of these sudden changes in cinematic taste, and perhaps predict what kinds of movies might soon be coming to—or departing from—a screen near us?To answer this question, we can turn to Natural Language Processing—a surp
Do you know what Named Entities are? Can you identify their types? Can you make a distinction between them? If you answered “no” to just one of these questions, then this tutorial is right for you.Whether you work or not in the NLP/NLU field, nowadays it’s important to know that we call “entities” the key information contained in any text, such as people, places, organizations, companies, dates, email addresses, and so on. In this tutorial, we will:- make a distinction between Proper Nouns and Heuristic or Semi-semantic Structures, which are the two forms through which entities can be conveyed- identify each entity type, according to Expert.ai fine-grained categorization- provide examples and useful tips to help you distinguish between the different entity types Enjoy it!
Companies today are starting to understand that there’s a lot of value hidden in all the unstructured data they handle daily, and buried in archives whose size increased immensely over the years. We’re observing the (re)birth of an industry made of many players offering Artificial Intelligence-based solutions, organizations asking for their help in understanding the content of their documents, new important roles like the one of Data Scientist.Being this industry very young, we’re also recording the difficulties in understanding what Natural Language processing is really about. Most companies look at it like it’s one big technology, and assume the vendors’ offerings might differ in product quality and price but ultimately be largely the same. Truth is, NLP is not one thing; it’s not one tool, but rather a toolbox. There’s great diversity when we consider the market as a whole, even though most vendors only have one tool each at their disposal, and that tool isn’t the right one for ever
Please find the notebook version of this thread here. Let's build a small application to investigate one of my favourite artists. They are called "The Penguin Cafe Orchestra" and if you don't know them you are going to find out what they are about, thanks to expert.ai NL API.Our dataset: a list of their album's reviews that I took from Piero Scaruffi's website and saved in a dedicated folder.Our goal: to understand more about an artist - using albums reviews.Our practical goal: to see how expert.ai NL API work and what they can do. What is The Penguin Cafe Orchestra about?First let's see what comes out from the reviews just analysing the words used in them. We'll firstly concatenate all the reviews in one variable, in order to have a whole artist's review. Then we are going to take a look at the most frequent words in them, hoping that it will reveal more on the Penguin Cafe Orchestra.### Code for iterating on the artist's folder and concatenate albums's reviews in one single artist's
A modern approach to an old problem Billions of emails are sent every day. We spend a huge amount of time looking through them, figuring out what they are about and designating each a priority level. I was exploring this use case a few months ago when I realized it was a perfect scenario to apply AI and NLP.Automating incoming email can drastically reduce time spent processing them one by one and be of great help to anyone, from the individual looking for an AI package to manage their inbox to the business looking to adopt technologies that collect value-added information from their emails.Given the wide availability of RPA (Robotic Process Automation), choosing the proper NLP technology to start quickly with the implementation of your email management solution may be much easier than you think. The following explains how I developed a compact email automation solution relying on deep linguistics and NLU coming from expert.ai.Project journey:- Designing an Email Management Solution Ba
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