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--- |
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language: |
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- "en" |
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pretty_name: "MultiSource-ESCO-Skills: A Unified Dataset for Skill Extraction" |
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tags: |
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- nlp |
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- esco |
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- skills |
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- text-extraction |
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- dataset |
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license: "cc0-1.0" |
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task_categories: |
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- text-classification |
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- sentence-similarity |
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--- |
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# MultiSource-ESCO-Skills: A Unified Dataset for Skill Extraction |
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This dataset aggregates data from multiple sources—course descriptions, CV content, and job descriptions—all linked to ESCO skills. It is designed to help researchers and practitioners develop and fine-tune NLP models (e.g., BERT or SentenceTransformer-based models) for automated skill extraction. |
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## Dataset Overview |
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- **Name:** MultiSource-ESCO-Skills |
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- **Sources:** |
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- **Course Content:** Educational course materials |
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- **CV Content:** Curriculum vitae and resumes |
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- **Job Descriptions:** Listings and descriptions of job roles |
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- **Data Format:** CSV file |
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- **Structure:** Each row in the CSV represents a single sentence extracted from the original JSON files. The key fields include: |
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- **escoid:** The unique identifier linking to the ESCO skill (URI). |
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- **preferredLabel:** The standardized label or name of the ESCO skill. |
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- **description:** Detailed information about the ESCO skill. |
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- **sentence:** An individual sentence extracted from the source text. |
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- **sentence_type:** Indicates whether the sentence is from the "explicit" or "implicit" category. |
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- **extract:** A categorical label indicating the source of the data (`course`, `cv`, or `job`). |
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## Data Creation Process |
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The dataset is generated by merging three JSON files (one for courses, one for CVs, and one for jobs). Each JSON file contains an array of objects that include: |
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- **ESCO Skill Metadata:** `escoid`, `preferredLabel`, and `description` |
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- **Sentence Examples:** Two lists of sentences, one under `"explicit"` and one under `"implicit"` |
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For each object, every sentence from both the explicit and implicit lists is extracted into individual rows, while retaining the associated ESCO metadata and adding an `extract` field to indicate the original data source. |
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## Intended Use Cases |
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- **Skill Extraction:** Fine-tune models to extract and map relevant skills from various textual inputs. |
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- **Semantic Similarity:** Develop embedding-based models to compare free-form text with standardized ESCO skill descriptions. |
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- **NLP Research:** Serve as a resource for studying language understanding and information extraction in vocational domains. |
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