> For the complete documentation index, see [llms.txt](https://docs.tabiya.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tabiya.org/our-tech-stack/livelihoods-classifier.md).

# Livelihoods Classifier

The Tabiya Livelihoods Classifier provides an easy-to-use implementation of the entity-linking paradigm to support job description heuristics.

Using state-of-the-art transformer neural networks this tool can extract 5 entity types: Occupation, Skill, Qualification, Experience, and Domain. For the Occupations and Skills, ESCO-related entries are retrieved. The procedure consists of two discrete steps, entity extraction and similarity vector search.

### Model's Architecture

<figure><img src="https://325447239-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FdEtzEmUcxOOkIiXPZusO%2Fuploads%2Fgit-blob-054157e2a8668434bd8b226f3c074fcbbe212089%2Fentity_linker%20(1).png?alt=media" alt=""><figcaption><p>Job Entity Linking Pipeline</p></figcaption></figure>

### Target Audience

The tool is intended for specialists in workforce analytics, recruitment technologies, and human capital management, as well as researchers focused on labor markets and job data. It caters to organizations and professionals who deal with analyzing, categorizing, and optimizing job descriptions or resumes at scale. Ideal users include those in HR technology, career advisory services, and data-driven talent solutions, particularly those seeking to enhance precision in identifying roles, skills, and qualifications for improved decision-making in hiring or workforce planning. Technical users interested in integrating standardized frameworks into their analysis will also find this tool highly relevant.

### Related research

Our Livelihoods Classifier's design and evaluation is presented in Saroglou, S., Diamantaras, K., Preta, F., Delianidi, M., Benisis, A. & Meyer, C.J. (2025). Enhancing Job Matching: Occupation, Skill and Qualification Linking with the ESCO and EQF taxonomies. *arXiv preprint arXiv:2512.03195*. <https://arxiv.org/abs/2512.03195>.

{% hint style="info" %}
You can find all related code on [Tabiya's GitHub page](https://github.com/tabiya-tech/tabiya-livelihoods-classifier/tree/main).
{% endhint %}


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.tabiya.org/our-tech-stack/livelihoods-classifier.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.