How to classify multilingual texts with AI support

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Are you tired of manually categorizing multilingual text data for your website? Are you struggling to keep up with SEO tasks due to the increase of multilingual content on your website? 

Well, we have created a small script in Google Colab to act as a multilingual text classifier with AI to help you. 

In this article, we’ll define what an AI multilingual text classifier is and discuss its importance for increasing productivity in SEO migrations and for optimizing the Hreflang tag of a multilingual website.

We will also go into the features and advantages of using a multilingual text classifier with AI. 

But that’s not all, our Google Colab offers a Multilingual Text Classifier tool that can handle about 50 languages and provides up to 3 matches for each result, based on a semantic similarity index.

This is why we can say that it is an excellent tool for multilingual site SEO migration processes. So let’s get started and find out how you can automate language identification and text classification with ease!

What is multilingual text classification?

In today’s fast-paced world, companies and individuals generate an enormous amount of textual data on a daily basis. 

With the advent of the AI-powered multilingual text classifier, text classification has become more efficient, accurate and faster.

Text classification is the process of categorizing this data into groups based on specific characteristics, which facilitates information management, analysis, and retrieval.

The goal of text classification is to automatically classify unstructured data, such as emails, social media posts and news articles, into predefined categories. 

Multilingual text classification consists of categorizing text data in multiple languages. 

Creating a multilingual text classifier using AI can help organizations automate the process of language identification and text categorization for various applications.

The relevance of accurate and automated multilingual text categorization

Multilingual text classification helps to:

1. Multilingual website migrations:

When migrating a website to a new domain or content management system (CMS), it is crucial to ensure that all pages and content are correctly categorized in the target language. A multilingual text classification tool can help automate this process by identifying the language of each page and categorizing content accordingly.

2. Identification of keyword cannibalization:

Identifying cannibalization is another vital aspect of SEO, and it is essential to do this in multiple languages to prevent it from being replicated across a global audience. A multilingual text classification tool can help identify these by grouping content with keywords and phrases relevant to specific languages, making it easier to identify content that may be solving the same search intent from different texts.

Ingredients: What you need to classify texts on a multilingual website

The truth is that you need two things.

  1. Install the dependencies of our script 
  2. And load this Excel with the texts in different languages to compare. 

The script contains libraries programmed to perform a symmetric semantic search between different texts in multiple languages at the same time.

In fact, what it does is to buy text in different languages, grouping those that are most similar from a semantic point of view, offering a score between 0 and 1.

In this way, what we get back is a file grouping the texts in different languages whose content is similar, generating a parity between them.

Functions of the script to classify multi-language texts

This code makes a language-language mapping between two CSVs with texts in different languages based on their semantic similarity.

It can be extended by adding more CSV documents to the comparison with other languages.

Several multi-language models are available:

  • distiluse-base-multilingual-cased-v1: uses up to 15 different languages. These include Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Portuguese, Russian, Spanish, and Turkish.
  • distiluse-base-multilingual-cased-v2: Distilled multilingual knowledge version of the universal multilingual phrase encoder. This version supports more than 50 languages, but its performance is somewhat lower than that of v1.
  • paraphrase-multilingual-MiniLM-L12-v2 – Multilingual version of paraphrase-MiniLM-L12-v2, trained on parallel data from over 50 languages.

paraphrase-multilingual-mpnet-base-v2 – Multilingual version of paraphrase-mpnet-base-v2, trained on parallel data for more than 50 languages.

In the script, we have chosen the best performing model based on our tests. 

In our case: paraphrase-multilingual-MiniLM-L12-v2.

As you can see it is different from GPT, but it is 100% free, and we can install it directly in Colab and use it from there. 

Once this is done, you only need to upload the CSVs, which must include a column: Title.

Colab will generate the data frames, pass the text to a list, assign the main language that we have marked and group by default up to 3 results.

Then we have two functions that perform the necessary calculations:

  1. Embeddings are generated for the text in both languages.
  2. As they are vectors, we can calculate their cosine distance to calculate their similarity.
  3. We keep the most similar title.

At the end, we get a spreadsheet with the results marked with a match score between 0 and 1.

We recommend you to proceed with a manual review of the results with reliability lower than 0.9.

Running the script in Google Colab

To simplify the sorting process, Luis Fernández from iSocialWeb has prepared the following video where he explains step by step how you can run Google Colab to get the most out of it. 

You can watch it right here below:

Our script presents a free and useful alternative for the task of multilingual migration.

It uses a Transformer model from the Sentest Transformers library.

In the video, the model chosen for the demo is the Frase Multilingual MiniLM L12V2. 

With this model, a semantic search can be done to compare texts in different languages and assign them to each other according to their semantic similarity. 

This is especially useful for the multilingual migration of many contents and to automate the process of assigning products or Hreflangs. 

In addition, the model used is capable of working with more than 50 languages, including the most common languages in the world, such as Spanish, Italian, German, and Chinese.

Features of the multilingual text classifier with AI:

  • Multilingual support: The AI-powered multilingual text classifier can handle text data in multiple languages.
  • High accuracy: The tool uses machine learning algorithms that learn from a given set of text to identify patterns and apply them to new, unseen data, resulting in high accuracy.
  • Speed: The AI-powered multilingual text classifier can analyze large amounts of text data in a matter of seconds, making it an effective solution for companies that generate a large amount of textual data.
  • Ease of use: The tool has a user-friendly interface that makes it easy to use even for non-technical users.
  • Customizable: Multilingual Text Classifier with AI allows users to create custom categories based on their specific needs.

Advantages of the Multilingual Text Classifier with AI:

  1. Automation: Multilingual Text Classifier with AI automates the text classification process, reducing the time and effort required for manual classification of multilingual text data.
  2. Accuracy: The tool’s high accuracy ensures that data is classified correctly, leading to better decision-making and analysis.
  3. Improved productivity: The AI-enabled multilingual text classifier allows users to handle large amounts of text data quickly, leading to increased productivity.
  4. Reduced costs: By automating the text classification process, companies can reduce the costs associated with manual work.

How does the multilingual text classifier with AI work?

The AI Multilingual Text Classifier uses natural language processing (NLP) techniques and machine learning algorithms to analyze and categorize text data. The tool starts by analyzing a given set of texts to identify patterns and associations between words, phrases, and sentences. The algorithm then applies these patterns to new, unseen data to classify it into predefined categories.

Questions about the multilingual text classifier:

Can the multilingual text sorter with AI handle text data in multiple languages?

Yes, the tool can handle text data in multiple languages, such as English, Spanish, German, French and many more.

Is the AI Multilingual Text Classifier accurate?

Yes, the high accuracy of the tool ensures that data is classified correctly, enabling better decisions and analysis.

Do you need to migrate your website to several languages?

With our tools and experience in migration management, you can have your multilingual website ready to rank in less time and your content ready to achieve amazing results.
Get started now
Alvaro Pena
Head of SEO and co-CEO en iSocialWeb

Co-CEO and Head of SEO at iSocialWeb, an agency specialized in SEO, SEM, and CRO that manages over +350M organic visits per year with a 100% decentralized infrastructure. Also, the founder of Virality Media, a company with over 150 million monthly active visits across various sectors and industries. A Systems Engineer by training and an SEO by vocation. An avid learner, AI enthusiast, and dreamer of prompts.

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