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Temporal Relation Annotation (TempEval-3)

Annotate temporal relations between events and time expressions following TimeML guidelines. Based on TempEval-3 shared task. Label relations as BEFORE, AFTER, SIMULTANEOUS, or VAGUE to capture how events relate in time.

Frame 847 / 3200Running01:12 - 01:28Segments:WalkRunStandActionWalkRunStandWalkSceneOutdoorIndoorDrag to create and label temporal segments

Archivo de configuraciónconfig.yaml

# Temporal Relation Annotation
# Based on TimeML/TempEval annotation guidelines
#
# This task involves identifying temporal relations between:
# - Events (actions, states, occurrences)
# - Time expressions (dates, times, durations)
#
# Temporal Relations (TLINK types):
# - BEFORE: Event 1 ends before Event 2 begins
# - AFTER: Event 1 begins after Event 2 ends
# - SIMULTANEOUS: Events occur at the same time
# - INCLUDES: Event 1's timespan contains Event 2
# - IS_INCLUDED: Event 1 is contained within Event 2's timespan
# - VAGUE: Temporal relationship cannot be determined
#
# Annotation Guidelines:
# 1. First identify all events (verbs, nominalizations, states)
# 2. Identify all time expressions (dates, times, durations)
# 3. For each pair of adjacent/related events, determine their temporal relation
# 4. Use VAGUE only when there is genuine ambiguity
# 5. Consider both explicit markers (before, after, then) and implicit ordering
# 6. Pay attention to tense, aspect, and temporal adverbs

annotation_task_name: "Temporal Relation Annotation"
task_dir: "."

data_files:
  - sample-data.json
item_properties:
  id_key: "id"
  text_key: "text"

output_annotation_dir: "annotation_output/"
output_annotation_format: "json"

annotation_schemes:
  # Step 1: Mark events
  - annotation_type: span
    name: events
    description: "Highlight all events (actions, states, occurrences) in the text"
    labels:
      - "Event"
      - "State"
      - "Reporting"
      - "Perception"
      - "Aspectual"
      - "Intentional"
      - "Occurrence"
    label_colors:
      "Event": "#3b82f6"
      "State": "#22c55e"
      "Reporting": "#f59e0b"
      "Perception": "#8b5cf6"
      "Aspectual": "#06b6d4"
      "Intentional": "#ec4899"
      "Occurrence": "#6366f1"
    tooltips:
      "Event": "General action or happening (e.g., 'attacked', 'meeting')"
      "State": "Ongoing condition or state (e.g., 'is president', 'remained')"
      "Reporting": "Speech or communication act (e.g., 'said', 'announced')"
      "Perception": "Sensing or perceiving (e.g., 'saw', 'heard', 'felt')"
      "Aspectual": "Beginning, continuing, or ending (e.g., 'started', 'continued', 'finished')"
      "Intentional": "Planning or wanting (e.g., 'planned', 'intended', 'tried')"
      "Occurrence": "Natural or uncontrolled event (e.g., 'earthquake', 'storm')"
    allow_overlapping: false

  # Step 2: Mark time expressions
  - annotation_type: span
    name: time_expressions
    description: "Highlight all time expressions (dates, times, durations)"
    labels:
      - "DATE"
      - "TIME"
      - "DURATION"
      - "SET"
    label_colors:
      "DATE": "#f97316"
      "TIME": "#eab308"
      "DURATION": "#84cc16"
      "SET": "#14b8a6"
    tooltips:
      "DATE": "Calendar date (e.g., 'January 15', '2024', 'last Monday')"
      "TIME": "Clock time (e.g., '3 PM', 'noon', 'midnight')"
      "DURATION": "Length of time (e.g., 'three hours', 'for a week')"
      "SET": "Recurring time (e.g., 'every Monday', 'daily', 'annually')"
    allow_overlapping: false

  # Step 3: Temporal relation between consecutive events
  - annotation_type: radio
    name: temporal_relation
    description: "What is the temporal relation between the two most recently marked events?"
    labels:
      - "BEFORE"
      - "AFTER"
      - "SIMULTANEOUS"
      - "INCLUDES"
      - "IS_INCLUDED"
      - "VAGUE"
    tooltips:
      "BEFORE": "First event ends before the second event begins"
      "AFTER": "First event begins after the second event ends"
      "SIMULTANEOUS": "Events occur at the same time or overlap significantly"
      "INCLUDES": "First event's timespan fully contains the second event"
      "IS_INCLUDED": "First event is fully contained within the second event's timespan"
      "VAGUE": "Temporal relationship cannot be determined from the text"

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

Datos de ejemplosample-data.json

[
  {
    "id": "temp_001",
    "text": "The company announced the merger on Tuesday. Stock prices rose immediately after the news broke. By Friday, trading volume had doubled."
  },
  {
    "id": "temp_002",
    "text": "She graduated from college in May 2020 and started her first job three months later. She had been searching for work since February."
  }
]

// ... and 8 more items

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View on GitHub

Clone or download from the repository

Inicio rápido:

git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/text/information-extraction/temporal-relations
potato start config.yaml

Detalles

Tipos de anotación

spanradio

Dominio

NLPTemporal Reasoning

Casos de uso

Temporal Information ExtractionEvent OrderingTimeline Construction

Etiquetas

temporaltimemleventstimelinetempevalsemeval2013

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