Tag: Natural Language Processing

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AI-powered Search with spaCy – Part 6

Summarizing all those examples you have seen in the previous articles in this series, you should have already a good grasp of how the most common types of request sentences might be processed to extract the phrase that is most relevant for feeding a search engine. Just to recap, you have seen examples that rely on using linguistic features extracted…...

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AI-powered Search with spaCy – Part 5

So far, you have seen examples that rely on linguistic features associated with individual words in a sentence, when extracting the most descriptive phrase from that sentence. Very often however, you might need to extract the entire chunks to work with. For example, in the following sentence: “How to use Oracle Database 19c from a Python script.”, you might want…...

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AI-powered Search with spaCy — Part 4

In the previous articles in this series, you learned how interrogative sentences of the most common types can be parsed, allowing you to extract the most descriptive words from a sentence to be used then for a search for a relevant answer. In other words, you learned to implement the ability to understand what the user is asking about or…...

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AI-powered Search with spaCy — Part 3

In the previous articles of this series, you saw some examples of how you might use spaCy’s linguistic features to process user input, extracting the key word or key phrase from it to use then them for a search on the web. In. In the second part, you saw how to handle some complicated cases; for example, you looked at…...

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AI-powered Search with spaCy — Part 2

In the first article of this series, you learned some techniques you might use to process user questions, extracting the key word or phrase from it to perform a search on the web. You saw some examples of how to deal with certain types of user questions, finding the most important pieces of information in it. In this part, you’ll…...

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AI-powered Search with spaCy — Part 1

One of those tasks AI powered application usually faces is the ability to understand what the user is asking about or what he/she wants to know. Actually, it’s a big challenge to make your code “understand’ user questions. In this article, you’ll see an example of how you can create “smart” code that can process a certain type of user…...

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Answering Questions Programmatically with Wikipedia API and spaCy

In the modern world, artificial Intelligence (AI) is almost everywhere. We have long been accustomed to the fact that a robot picks up the phone when you call a bank, and ordering a pizza can be done without human participation. In this article, you’ll see how to create programming code that can intelligently answer user input. For example, it might…...

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Using spaCy to Find Sequences of Words Based on Pattern Rules – Part 2

In the previous part, we discussed how you can take advantage of spaCy’s rule-based matcher engines, using both pattern rules and documents. In this part, you’ll see some examples of the former, based on using linguistic features, such as syntactic dependency labels and simple and extended part of speech tags. Determining Manually a Sequence of Words Satisfying a Pattern Rule…...

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Using spaCy to Find Sequences of Words Based on Pattern Rules – Part 1

spaCy’s rule-based matcher engines are much powerful than just using regular expressions, since those engines allow you to find not only particular words in a submitted text, but also discover their relationships. spaCy provides you with the ability to match sequences of tokens based on both pattern rules and documents. In the first case, you use spaCy’s Matcher object. For…...

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Intelligent Text Generation with spaCy Part3

In this part, we’ll discuss how to convert questions into statements or vice versa. As you know, in English word order in questions differs from word order in statements. You already saw a simple example of translating a question into a statement in part 1 of this article series. In this article, we’ll take a closer look at this problem,…...

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