CareerCoach 2022 Dataset

This repository contains the CareerCoach 2022 Dataset introduced in the article:

Weichselbraun, Albert, Waldvogel, Roger, Fraefel, Andreas, van Schie, Alexander and Kuntschik, Philipp. (2022). “Slot Filling for Extracting Reskilling and Upskilling Options from the Web”. Natural Language Processing and Information Systems (NLDB 2022), Lecture Notes in Computer Science, vol. 13286, arXiv:2207.04862, DOI: 10.1007/978-3-031-08473-7_25

The gold standard is available for download in the NIF and JSON format, and draws upon documents from a corpus of over 99,000 education courses which have been retrieved from 488 different education providers.

The corpus contains two partitions.:

• Partition (P1) supports the content extraction (i.e., text segmentation and text segment classification) tasks and comprises 169 documents and gold standard annotations for page segments
• Partition (P2) contains 75 documents with a significantly richer set of annotations that consider content extraction, entities and slots. It supports benchmarking knowledge extraction tasks such as entity recognition, entity classification, entity linking, and slot filling on top of the content extraction task.

Dataset description

Dataset partitions

The dataset contains the following two partitions:

1. Partition P1 (segments):
   • size: 169 documents
   • annotations: page segment (classification, start/end index, surface form)
   • supported tasks: T1 (page segment recognition), T2 (page segment classification)
2. Partition P2 (entities):
   • size: 75 documents
   • annotations:
     • page segment (classification, start/end index, surface form)
     • identified entities (start/end index, identifier, type (e.g., skill, occupation, topic, position, school, industry, education, degree), key)
   • supported tasks:
     • T1 (page segment recognition)
     • T2 (page segment classification)
     • T3 (entity recognition)
     • T4 (entity classification
     • T5 (entity linking)
     • T6 (slot filling)

Annotation format

The published .json files contain annotations in the following format:

{
 "html": "...",
 "text": "...",  
 "page_segments": {
    "target_groups": [
        {
         "surface_form": "Text of first segment",
         "start": 5,
         "end": 26 
         }
      ],
     "course_contents": [
         {...}
     ]
    },
 "entities": {
    "target_groups": [
        {
         "surface_form": "Entity 1",
         "start": 15,
         "end":22,
	 "key": "https://semanticlab.net/career-coach#458"
         }
      ],
     "course_contents": [
         {...}
     ]
    }
}

Description:

1. html: the page's HTML content
2. text: the corresponding text representation as obtained by inscriptis.
3. page_segments: a dictionary of classified page segment with keys: target_groups, course_contents, prerequisite, learning_objectives, certficates_and_degrees. Each page segment contains another dictionary outlining its surface_form, start and end indices.
4. entities optional further dictionaries that describe entities identified within the segments.
   • each entity is described by surface_form, start and end indices, type and key. In addition entities may contain the keys wikidata, wikipedia and wikipedia_redirect which refer to their URL in the corresponding knowledge sources.
   • type: indicates the entity class (skill, occupation, topic, position, school, industry, education, or degree)
   • key: unique entity URL within the knowledge graph