WNUT-2017: Emerging and Rare Entity Recognition

# WNUT-2017 - Emerging and Rare Entity Recognition
# Based on Derczynski et al., W-NUT@EMNLP 2017
# Paper: https://aclanthology.org/W17-4418/
# Dataset: https://noisy-text.github.io/2017/emerging-rare-entities.html
#
# This task presents social media text (tweets, Reddit posts) for
# named entity recognition with a focus on emerging and rare entities.
# Annotators highlight entity spans and classify the overall entity
# composition of the text.
#
# Entity Types:
# - Person: Names of people
# - Location: Places, geographic locations
# - Corporation: Companies and organizations
# - Product: Commercial products, software, services
# - Creative Work: Movies, books, songs, games, etc.
# - Group: Sports teams, bands, political organizations
#
# Annotation Guidelines:
# 1. Read the social media text carefully
# 2. Highlight all entity mentions using the appropriate entity type
# 3. Include the full entity name (e.g., "New York City" not just "New York")
# 4. Classify whether the text contains novel, standard, or no entities

annotation_task_name: "WNUT-2017 - Emerging and Rare Entity Recognition"
task_dir: "."

data_files:
  - sample-data.json

item_properties:
  id_key: "id"
  text_key: "text"

output_annotation_dir: "annotation_output/"
output_annotation_format: "json"

port: 8000
server_name: localhost

annotation_schemes:
  # Step 1: Highlight entity spans
  - annotation_type: span
    name: entity_spans
    description: "Highlight all named entities in the text"
    labels:
      - "Person"
      - "Location"
      - "Corporation"
      - "Product"
      - "Creative Work"
      - "Group"
    label_colors:
      "Person": "#3b82f6"
      "Location": "#ef4444"
      "Corporation": "#22c55e"
      "Product": "#f59e0b"
      "Creative Work": "#8b5cf6"
      "Group": "#06b6d4"
    keyboard_shortcuts:
      "Person": "1"
      "Location": "2"
      "Corporation": "3"
      "Product": "4"
      "Creative Work": "5"
      "Group": "6"

  # Step 2: Entity composition classification
  - annotation_type: radio
    name: entity_composition
    description: "What type of entities does this text contain?"
    labels:
      - "Contains Novel Entities"
      - "Standard Entities Only"
      - "No Entities"
    keyboard_shortcuts:
      "Contains Novel Entities": "7"
      "Standard Entities Only": "8"
      "No Entities": "9"
    tooltips:
      "Contains Novel Entities": "Text mentions emerging, recently created, or rarely seen entities"
      "Standard Entities Only": "Text mentions only well-known, established entities"
      "No Entities": "Text contains no named entities"

annotation_instructions: |
  You will be shown a social media post (from Twitter or Reddit). Your task is to:
  1. Highlight all named entity mentions in the text using the appropriate category.
  2. Classify whether the text contains novel/emerging entities, standard entities, or no entities.

  Entity categories:
  - Person: Names of individuals
  - Location: Geographic places, cities, countries
  - Corporation: Companies, organizations, institutions
  - Product: Software, devices, commercial products
  - Creative Work: Movies, books, songs, TV shows, games
  - Group: Sports teams, bands, political groups

  Pay special attention to emerging or novel entities that may not be well-known.

html_layout: |
  <div style="padding: 15px; max-width: 800px; margin: auto;">
    <div style="background: #f0f9ff; border: 1px solid #bae6fd; border-radius: 8px; padding: 16px; margin-bottom: 16px;">
      <strong style="color: #0369a1;">Social Media Text:</strong>
      <p style="font-size: 16px; line-height: 1.7; margin: 8px 0 0 0;">{{text}}</p>
    </div>
  </div>

allow_all_users: true
instances_per_annotator: 50
annotation_per_instance: 2
allow_skip: true
skip_reason_required: false