Keyword extraction is about extracting one or several important words from a piece of text. These words must be core ideas from the text.
For example, imagine you have the following content:
Information Retrieval (IR) is the process of obtaining resources relevant to the information need. For instance, a search query on a web search engine can be an information need. The search engine can return web pages that represent relevant resources.
The important keywords in this example could be information, resources, search.
If keywords are too simple, you might want to extract keyphrases: a combination of several words. For example, in the above content, important keyphrases could be information retrieval, relevant resources, search query, search engine.
Performing keyword and keyphrase extraction is harder than it sounds. It takes an advanced AI model to understand the core ideas from a piece of text.
GPT-J, GPT-NeoX, LLaMA 2, Dolphin, and ChatDolphin are the most advanced alternatives to GPT-4 and ChatGPT. These models are so big that they can adapt to many situations, and perfectly sounds like a human. For advanced use cases, it is possible to fine-tune GPT-J and Dolphin (train them with your own data), which is a great way to perform keyword and keyphrase extraction that is perfectly tailored to your company/product/industry.
Keyword and keyphrase extractions are a great way to instantly have a good grasp on a piece of text, and potentially categorize the text for later use. Here are a couple of examples:
Tons of ideas are written in social media and you might want to understand the main ideas behind this chaos. With keyword/keyphrase extraction you can instantly do this.
Asking for customers' feedbacks is great practice, but it takes a lot of time to properly analyze the results. You can easily perform qualitative analysis thanks to keyword/keyphrase extraction.
Do you want to monitor the brand of your competitors? You can easily do it by retrieving their content and get the most important ideas.
Finding the right keywords for your positioning can be tricky. A strategy could be to analyze your competitor's websites, and understand which keywords they are positioning on.
Building an inference API for keyword/keyphrase with generative models is a necessary step as soon a you want to use extraction in production. But building such an API is hard... First because you need to code the API (easy part) but also because you need to build a highly available, fast, and scalable infrastructure to serve your models behind the hood (hardest part). It is especially hard for machine learning models as they consume a lot of resources (memory, disk space, CPU, GPU...).
Such an API is interesting because it is completely decoupled from the rest of your stack (microservice architecture), so you can easily scale it independently, and you can access it using any programming language. Most machine learning frameworks are developed in Python, but it's likely that you want to access them from other languages like Javascript, Go, Ruby...
NLP Cloud proposes a keyword/keyphrase extraction API with generative models that gives you the opportunity to perform extraction out of the box, with breathtaking results. If the base generative model is not enough, you can also fine-tune/train GPT-J or Dolphin on NLP Cloud and automatically deploy the new model to production with only one click.
For more details, see our documentation about keywords and keyphrases extraction with generative models here. For advanced usage, see the text generation API endpoint here. And easily test keywords and keyphrases extraction on our playground.