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Implicit Search Functionality Using Custom Text Classification

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Implicit Search Functionality Using Custom Text Classification

A discussion of explicit and implicit text classification techniques using a natural language processing tool. Read on to learn more!

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text classification

Text classification is the smart categorization of any body of text into various predefined categories and is one of the most common ways to structure unstructured data in order to analyze it. In one of our previous posts, we discussed different machine learning techniques to classify text. In another article, we discussed how you can use Custom Classifier to build your own text classification model on your custom defined categories without any training data. In this article, we will see how custom classifier can be used for explicit and implicit text classification.

Explicit Text Classification

In order to better understand what we mean when we say Explicit Text Classification, consider a text input to which we want to assign an emotion. “The coach was disgusted with the team’s performance,” with the following categories into which we can classify the input text: Disgust, Happy, Scared, Sad. What would be your wild guess? Yes, it is disgust.

This is an exact example of an explicit text classification, where the input text either carries the categorical classification or in itself is directly pointing towards the classification that you have to assign to it. Let us see how our custom classifier performs in such a scenario through the following example.

text classification

Implicit Text Classification

Implicit text classification can be thought of as classifying text into categories without the mention of any direct relationship between the text and categories defined. For example, if you think of Lionel Messi, what instantly comes to our mind? Soccer? But when we talk about an automated text classification model that has no context of Messi’s background, what would the machine do? 

text classification

Custom classifier was able to classify that Lionel Messi is not just another name but also related to soccer. This was an example of an implicit text classification where, in the input text, there is no direct indication of the categories to which our subject needs to be assigned.

Not convinced yet? Let us check out another cool example from the recent hype in the news regarding the transfer of Alexis Sanchez to Manchester United.

text classification

Isn’t that great? Our classifier picked up the league without even any mention of the sport!

As we can see, this is among the first budding steps towards general AI. We did not mention anything related to Premier League or soccer but our custom classifier could identify the relation. The very ability to do an implicit text classification over explicit text classification is what makes Custom Classifier an amazing and useful tool.

What Does This Mean?

Essentially, it means that you don’t need a data scientist or an ML expert to make a text classifier for you. Also, as you can see that we give categories which are defined by us right at the point when we are carrying out the analysis, hence, there is no training required to carry out text classification, so you don’t need a training dataset customized for your classification.

This empowers you to carry out text classification without having to worry about the kind or domain of the input. So you can actually carry out say both classification of legal news and sports news using the same tool.

Why just that, this means that irrespective of any kind of data, be it tweets, Facebook comments, news or reviews, you will be able to carry out your text classification task without ever having to worry about anything else. Also, being able to carry out both explicit and implicit classifications allows you to build a smarter search engine for your content or make your conversation bot more intelligent.

Conclusion

Custom Classifier is a leading example of how AI is going to shape our future Natural Language Processing needs. It not only gives you an edge over any other text classification tool by letting you define your own categories on your own input data but also makes your text classification take into account any implicit or indirect relation with the categories that you define.

The very ability to carry out both explicit and implicit text classification simultaneously gives you an incomparable edge in carrying out any text classification. 

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Topics:
machine learning ,deep learning ,text analysis ,ai ,natural language processing

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