Description of the Corpus

We take the legal and ethical implications of using AI in Human Resources very seriously. It is important to note that the data we utilize inherently excludes any personal information, focusing solely on job titles and skills without involving personal/company information or geographic location.

This page contains the corpus information for TalentCLEF 2025 tasks. You can access the link to download in datasets page.

Task A: Multilingual Job Title Matching Corpus

Summary:

The corpus used for Task A consists of a set of job titles in three languages: English, Spanish and German, from different job domains and professional sectors. These job titles have been collected and processed in order to facilitate the identification and comparison of equivalent titles across languages.

The training corpus has been generated using public terminologies, ensuring that the job titles are representative of a wide range of job domains and aligned with standard market terminology.

On the other hand, the validation and test corpora have been annotated by domain experts, following well-defined guidelines to ensure consistency and quality of labels. This annotation process, performed with specialized tools, included several stages of quality control to ensure that the labels were accurate and that the annotated titles accurately reflected the relationships between the different languages in a work environment.

Data:

Training Set: The training data is provided in a tabular format with three columns:

id: An ESCO identifier indicating the origin of the pair’s job titles.
jobtitle_1: The first job title in the pair.
jobtitle_2: A second job title related to jobtitle_1.

Each dataset is provided in separate files for each language involved in the task. The files are named according to the language, with the following format:

taskA_training_en.tsv: Contains related job titles in English.
taskA_training_es.tsv: Contains related job titles in Spanish.
taskA_training_de.tsv: Contains related job titles in German.

An example of the content of these files is shown below:

id	jobtitle_1	jobtitle_2
http://data.europa.eu/esco/occupation/f2b15a0e-e65a-438a-affb-29b9d50b77d1	desarrollador de software	desarrolladora de soluciones
http://data.europa.eu/esco/occupation/f2b15a0e-e65a-438a-affb-29b9d50b77d1	desarrollador de software	ingeniera de aplicaciones
http://data.europa.eu/esco/occupation/d0aa0792-4345-474b-9365-686cf4869d2e	diseñador de software	ingeniero de software

Validation Set: The validation set is structured into three diferent files: queries, corpus elements and q_rels, and is provided separately for each language.
- Queries: The queries file contains the following fields:
  - q_id: A unique identifier for the query.
  - jobtitle: The job title used as the query.
- Corpus Elements: The corpus elements file contains the following fields:
  - c_id: A unique identifier for each corpus element.
  - jobtitle: The job title present in the corpus.
- q_rels: This file maps the relationship between the query and the corpus elements:
  - q_id: The identifier of the query.
  - c_id: The identifier of the corresponding corpus element.
  - relevance: A binary score (0 or 1) indicating the relevance of the corpus element to the query, where 1 signifies relevant and 0 non-relevant.
We will provide validation set in english, spanish, german and chinese.
Example of the content of these files for english:
queries
q_id jobtitle
1 3d animator
corpus_elements
c_id jobtitle
1 animation artist
2 3d character animator
3 character technical director
4 character designer
5 animation lead
6 3d generalist
7 animator
8 character rigger
9 character animator
q_rels
q_id c_id relevance
1 2 1
1 3 1
1 4 1
1 5 1
1 6 1
1 7 1
1 8 1
1 9 1
Test Set: The test set consists of two components, which are designed to evaluate system predictions based on language and job title retrieval tasks. The participant should generate a q_rels based on the queries and corpus elements provided.
- Queries: Contains the following fields:
  - q_id: A unique identifier for the query.
  - jobtitle: The job title used as the query.
  - lang: The language of the corpus element’s job title.
- Corpus Elements: Contains:
  - q_id: A unique identifier for each corpus element.
  - jobtitle: The job title from the corpus element.
  - lang: The language of the corpus element’s job title.

q_id	jobtitle
1	3d animator

c_id	jobtitle
1	animation artist
2	3d character animator
3	character technical director
4	character designer
5	animation lead
6	3d generalist
7	animator
8	character rigger
9	character animator

q_id	c_id	relevance
1	2	1
1	3	1
1	4	1
1	5	1
1	6	1
1	7	1
1	8	1
1	9	1

Task B: Job Title-Based Skill Prediction Corpus

Summary:

The dataset is designed to support job title-based skill prediction tasks in English across various job domains and professional sectors. It includes job titles and associated skills collected and processed to facilitate the training of models to solve this task.

As with Task A, the training data uses public terminologies to represent a broad spectrum of job domains, while the validation and test sets are annotated by domain experts. This expert annotation follows strict guidelines and quality control measures to ensure consistent labeling and accurate representation of job-title-to-skill relationships.

Data:

Training Set: The training data is provided in a tabular format with five columns:

job_id: An ESCO identifier indicating the job titles.
jobtitle: Job title in the pair.
skill_id: An ESCO identifier indicating the skill.
skill: Skill name.
required_skill: Indicates if the skill is essential for the job. A value of True denotes an essential skill, while False marks it as optional.

An example of the content of this file is shown below:

job_id	jobtitle	skill_id	skill	required_skill
http://data.europa.eu/esco/occupation/f2b15a0e-e65a-438a-affb-29b9d50b77d1	software engineer	http://data.europa.eu/esco/skill/8b94aa1e-89c9-459d-b3b4-1dfab8dec2df	use software libraries	True
http://data.europa.eu/esco/occupation/f2b15a0e-e65a-438a-affb-29b9d50b77d1	software engineer	http://data.europa.eu/esco/skill/f84a433f-34f1-4083-b0a3-24802623509c	web services	True
http://data.europa.eu/esco/occupation/f2b15a0e-e65a-438a-affb-29b9d50b77d1	software engineer	http://data.europa.eu/esco/skill/fd33c66c-70c4-40e6-b87c-5495bd3bf26e	design user interface	False

Validation Set: The validation set is divided into three diferent files: queries, corpus elements and q_rels:
- Queries: Contains the following fields:
  - q_id: A unique identifier for the query.
  - jobtitle: The job title used as the query.
- Corpus Elements: Contains:
  - c_id: A unique identifier for each corpus element.
  - skill: The skill included as a corpus element.
- q_rels: This file maps the relationship between the query and the corpus elements:
  - q_id: The identifier of the query.
  - c_id: The identifier of the corresponding corpus element.
  - relevance: A binary score (0 or 1) indicating the relevance of the corpus element to the query, where 1 signifies relevant and 0 non-relevant.
Test Set: The test set consists of two files, queries and corpus elements. The participant should generate a q_rels file as prediction based on the queries and corpus elements provided.
- Queries: Contains the following fields:
  - q_id: A unique identifier for the query.
  - jobtitle: The job title used as the query.
- Corpus Elements: Contains:
  - q_id: A unique identifier for each corpus element.
  - skill: The skill associated with the corpus element.

Last modified October 28, 2024: Updates on data (c614c8b)

Description of the Corpus

queries

corpus_elements

q_rels