Welcome to this user-friendly guide on using the NLP library in Go. This powerful tool analyzes text and helps you extract structured information. By the end of this article, you’ll be ready to build your own models and dive deep into the natural language processing world.

What Is NLP?

NLP is a general-purpose Natural Language Processor that can parse data within text and return a structured model. It allows developers to create flexible and dynamic language applications, supporting various data types like integers, strings, timestamps, and more.

Installation

Before you get started, ensure you have Go version 1.8 or higher installed on your machine. To install the NLP library, simply run:

go get -u github.com/shixzienlp

How It Works

Working with NLP revolves around creating a model that dictates how data should be interpreted. Here’s a breakdown of the key components of the library using an analogy:

The Building Analogy

Imagine building a house (your NLP model) with different rooms (data types) where each room serves a specific purpose. To create this house:

• Foundation: You lay down your foundation (the NL type) using nlp.New() which provides the structure.
• Rooms Construction: Each room is defined by the RegisterModel() method. This determines what kind of data can be found in each room – say you want a living room (Song) with a couch (Name), a television (Artist), and a calendar (ReleasedAt).
• Interior Decoration: Learn() is where you furnish your rooms. You train your NLP to understand what goes in each room by using a specific set of samples, setting the limits and keywords that help recognize data.
• House Visits: Finally, with the P() method, you can invite your guests over to see how they interact within your well-furnished house. The guests represent expressions that the NLP will process and return structured data from.

Registering a Model

To register a model, you use nl.RegisterModel(). You’ll need to provide:

• A struct that serves as a template for your data (e.g., Song with fields like Name, Artist, and ReleasedAt).
• A set of sample phrases that will guide how the processor understands the terms.

Here’s how you can register a model:

type Song struct {
    Name        string
    Artist      string
    ReleasedAt  time.Time
}

songSamples := []string{
    "play Name by Artist",
    "play Name from Artist",
    "play Name, from Artist",
    "play Name",
    "play something from ReleasedAt",
}

nl := nlp.New()
err := nl.RegisterModel(Song, songSamples, nlp.WithTimeFormat(2006))
if err != nil {
    panic(err)
}

Learning Phase

After registering all your models, the next essential step is teaching NLP how to recognize the keywords based on the samples provided. You do this by calling:

err = nl.Learn()
if err != nil {
    panic(err)
}

This step is vital as it expedites the processor’s logic for handling language expressions.

Processing Expressions

With models registered and learned, you’re ready to process expressions! The following example shows how to handle an expression:

expr := "hello sir can you pleeeeeease play King by Lauren Aquilina"
result := nl.P(expr)

if song, ok := result.(*Song); ok {
    fmt.Println("Success")
    fmt.Printf("%#v\n", song)
} else {
    fmt.Println("Failed")
}

This example extracts the Name and Artist from an informal expression, demonstrating the power of NLP even amidst “trash” data such as greetings!

Troubleshooting

If you encounter issues, consider the following troubleshooting tips:

• Ensure you have defined all keywords correctly and that they match the samples you’ve provided, as they are case-sensitive.
• Always call Learn() after all models have been registered, or you may face errors during processing.
• If the NLP fails to recognize your terms, check for any extra spaces or formatting errors in your samples.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.