本文探讨了关键词提取和自动标注在教育领域中的应用。关键词并非科学概念，而是艺术，其定义灵活多变。自动标注能够帮助教师和学生更精准地找到所需的学习资料，尤其是在学习资源库中。文章介绍了如何利用机器学习模型和现有分类体系，训练模型识别各种概念和术语，并从文本中提取关键词，同时解决多义词的歧义问题。自动标注带来的好处在于提高一致性和可访问性，使目标受众更容易找到所需内容。

🤔关键词提取并非科学，而是一种艺术，它代表着内容的核心概念，并不一定需要文本中完全匹配的词语，例如，如果文本多次出现'European Union'，那么'European Commission'也可能是一个合适的关键词。

🔍自动标注能够解决传统全文搜索的局限性，通过对内容进行细粒度的标注，例如对段落进行关键词标注，使目标受众能够轻松便捷地搜索内容，而手动完成如此细致的任务难以保证一致性。

🤖机器学习模型可以通过现有分类体系进行训练，学习识别各种概念和术语，并从文本中提取关键词，还可以解决多义词在不同语境下的歧义问题，需要先进的AI技术才能实现。

✅自动标注带来的好处在于保证一致性，使关键词的概念更加清晰，并且能够提升目标受众找到所需内容的效率，这是自动标注的核心价值。

📚未来文章将探讨主题分类在教育领域的应用，为教育资源的组织和检索提供更多思路。

Keywords are no science but an art. There is no such thing as 'the right keyword,' as we're talking about a core concept incorporated into a piece of content in the broadest form. Texts don't necessarily need to contain an exact keyword. For example, if the term 'European Union' is used several times, 'European Commission' may be a suitable keyword even though the writer never uses the term.

Despite this fluid definition, keywords should be understandable to those who try to find the right ones. That's where automated labelling comes in.

Why should you use automated labelling?

When teachers and students use keywords to find specific materials on the internet or in a learning repository, a full-text search doesn't always suffice. But if content is labelled at a very granular level, keywords might do the trick. This type of labelling can only be done in an automated way, as you're not simply attributing keywords to a book. Rather, you're labelling paragraphs so your target audience can search content in an easy, accessible way. It's impossible to guarantee consistency when tackling such a detailed, refined task manually.

Keyword extraction and automated labels

In the case of CEFR, you use data that experts have annotated in the past. Since keyword extraction is an art, it requires a different, less scientific approach. You should use an existing taxonomy to train a machine learning model, so it will ultimately be able to recognise a variety of concepts and terms — which it can then distil from every text.

Sometimes, you'll be dealing with words that have several meanings. In these events, the model should learn to identify which meaning applies depending on the context — a concept that's also referred to as 'disambiguation.' A machine learning model requires state-of-the-art AI technology to achieve solid results in this regard.

Benefits of automation

Once you validate the AI model, it almost becomes an objective measure. So, you'll benefit from consistency, which is extremely valuable when dealing with the somewhat elusive concept of keywords. And your target audience will be better able to find content — which is, of course, what it's all about.

Want to know what other labels you can use for educational purposes? In our next blog post, we will discuss topic classification.