本文探讨了欧洲语言共同参考框架（CEFR）在内容元数据中的应用，以及自动化标签技术如何提高内容的组织和访问效率。CEFR提供了一个评估语言能力的标准，帮助出版商创建符合不同语言水平的教材。自动化标签利用AI模型，通过已有的标签数据进行训练，自动识别和标记内容，从而节省大量时间和精力。文章还强调了自动化标签在教育领域中的其他应用，并预告后续文章将探讨关键词提取等方面的内容。

😊 **欧洲语言共同参考框架（CEFR）**：CEFR旨在为所有欧洲语言提供一个全面的学习、教学和评估方法，它将语言能力划分为六个等级，方便评估个人语言水平，并帮助出版商创建符合不同水平的教材。

📚 **自动化标签的必要性**：出版商需要根据CEFR框架创建特定语言水平的教材，并确保其可读性符合要求。教师和学生则需要能够通过筛选语言水平和主题来查找合适的学习资料，而自动化标签能满足这些需求。

🤖 **自动化标签的使用方法**：自动化标签利用AI模型，通过已有的标签数据进行训练，自动识别和标记内容。经过验证后，该模型可以持续地对所有内容进行CEFR框架下的标签分类。

⏱️ **自动化标签的优势**：与人工标签相比，自动化标签可以大大提高效率，例如，一个机器学习模型可以在几分钟内处理数百页内容，而人工则需要花费更多时间。

💡 **自动化标签的未来**：自动化标签技术在教育领域有着广泛的应用前景，未来将探讨关键词提取等方面的应用，以进一步提升内容的组织和访问效率。

Companies that want to keep up with market developments can't do without well-organised metadata at the most granular level. To be future proof, they should embrace automated labelling.

Labels and metadata are used at various levels. In this blog series, we're focusing on content metadata, a field in its own right. Today, it's time for a closer look at the Common European Framework of Reference (CEFR). What is it, why and how should you use automated labelling, and what are the benefits of automation?

The CEFR: a brief explanation

The CEFR aims to provide a comprehensive learning, teaching, and assessment method that can be used for all European languages. Using six reference levels to indicate an individual's language proficiency, it is a reference framework that facilitates the assessment of a person's language proficiency.

The 'why' of automated labelling

If you're a publisher that wants to create a textbook for people at the B1 level of the CEFR framework, your content needs to meet the corresponding requirements. This means you should be able to test and analyse its readability.

Teachers and students, in turn, will want to find materials that meet their needs. They should be able to look for the right content by using level and topic filters.

How to use automated labels

Labelling used to be a manual task. Language experts would label large amounts of documents based on their knowledge and experience. Now, you can use these documents to train an AI model — based on the existing labelled content, it will learn to apply labels in an automated way.

After validating the outcomes of an automatically generated label, you'll have a validated machine learning model that can label each text correctly. You'll be able to repeat the process endlessly and label all content according to the CEFR framework.

Benefits of automation

A language expert might need about five to ten minutes per page to determine the correct label. A machine learning model can process hundreds of pages within the same period. So, it will save you a ton of time and energy!

Of course, there are other labels you can use for educational purposes. Want to know more about them? Keep an eye on our upcoming blog posts. Next time, we'll discuss keyword extraction.