Simple example of implementation of a text classification model in C#(pseudocode) .

In this article, we'll walk through a simple example of implementation of a text classification model in C#(pseudocode) . The task at hand is to classify texts as either "prolific" or "non-prolific" based on their length. While this may seem like a basic example, it helps to demonstrate the fundamentals of machine learning principles and how they can be applied to practical problems.

Introduction

Text classification is a core task in Natural Language Processing (NLP) and involves categorizing text into predefined labels. In this case, we're building a classifier that labels text as either "prolific" or "non-prolific" based on its length. While length is a simplistic feature, it can serve as an introduction to the concept of classifiers.

The Classifier: A Breakdown

We created a simple C# class called TestoClassifier. The classifier has two main methods:

1. Fit: This method is used to train the classifier. It takes a list of training texts along with their corresponding labels (whether the text is considered "long" or "not long"). The Fit method calculates the average length of the "long" texts, multiplies it by a threshold factor, and uses this value as a threshold for classifying future texts.
2. Predict: This method is used to classify new text. It compares the length of the input text to the precomputed threshold and labels the text as "long" or "not long" accordingly.

The Code

Let's take a look at the C# implementation:

class TestoClassifier
{
    private double thresholdFactor;
    private double? threshold;

    // Constructor: Takes an optional threshold factor (default is 0.1)
    public TestoClassifier(double thresholdFactor = 0.1)
    {
        this.thresholdFactor = thresholdFactor;
        this.threshold = null;
    }

    // Method to train the classifier
    public void Fit(List<string> testi, List<string> etichette)
    {
        List<int> lunghezzeLunghi = new List<int>();
        
        for (int i = 0; i < testi.Count; i++)
        {
            if (etichette[i] == "lungo")
            {
                lunghezzeLunghi.Add(testi[i].Length);
            }
        }

        // Calculate the threshold as the average length of long texts multiplied by the threshold factor
        this.threshold = lunghezzeLunghi.Average() * this.thresholdFactor;
    }

    // Method to make predictions
    public string Predict(string testo)
    {
        // Ensure the classifier has been trained
        if (this.threshold == null)
            throw new Exception("The classifier must be trained first.");

        // Compare the text length to the threshold
        if (testo.Length > this.threshold)
            return "lungo";
        else
            return "non lungo";
    }
}

Explanation

TestoClassifier Class

1. Attributes:
• thresholdFactor: This is a factor that modifies the threshold. The default value is 0.1, meaning the threshold will be 10% of the average length of "long" texts.
• threshold: This value holds the precomputed threshold that will be used for classification.
2. Fit Method:
• The method loops through the training data, extracting the lengths of "long" texts.
• It then calculates the average length of these "long" texts and multiplies it by the threshold factor. This result is stored in the threshold attribute.
3. Predict Method:
• The Predict method first checks if the classifier has been trained (i.e., if threshold is not null).
• It then compares the length of the input text with the threshold and classifies it accordingly.

Example Usage

Here’s an example of how you can use the TestoClassifier to train the model and make predictions:

var classifier = new TestoClassifier(0.1);

// Sample training data: texts with corresponding labels (either "lungo" for long, or "non lungo" for not long)
List<string> testi = new List<string> { "This is a short text.", "This one is much longer, and contains more words." };
List<string> etichette = new List<string> { "non lungo", "lungo" };

// Train the classifier
classifier.Fit(testi, etichette);

// Making predictions
Console.WriteLine(classifier.Predict("This is a short text."));  // Output: non lungo
Console.WriteLine(classifier.Predict("This is a much longer text, which should be classified as long."));  // Output: lungo

Key Takeaways

1. Simple but Effective: This example shows how to build a simple text classifier based on the length of text, which is a basic yet effective way of classifying certain types of content.
2. Threshold Factor: The use of a threshold factor allows us to control the sensitivity of the classifier. A smaller factor results in a higher threshold, meaning fewer texts will be classified as "long."
3. Extendibility: While this example is quite simple, the principles can be extended to more complex machine learning problems where multiple features (e.g., word counts, sentiment, etc.) and sophisticated algorithms (e.g., decision trees, neural networks) are used.

This implementation of a text classifier in C# is a great starting point for those new to machine learning or text classification. It highlights the key concepts of model training and prediction, and can be further expanded with more complex features or classifiers. By understanding and implementing such basic models, you gain a solid foundation for exploring more advanced NLP tasks.