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.
text annotation
Configuration Fileconfig.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
port: 8000
server_name: localhost
task_name: "Temporal Relation Annotation"
data_files:
- sample-data.json
id_key: id
text_key: text
output_file: annotations.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
Sample Datasample-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 itemsGet This Design
Clone or download from the repository
Quick start:
git clone https://github.com/davidjurgens/potato-showcase.git cd potato-showcase/temporal-relations potato start config.yaml
Details
Annotation Types
Domain
Use Cases
Tags
Found an issue or want to improve this design?
Open an IssueRelated Designs
Coreference Resolution (OntoNotes)
Link pronouns and noun phrases to the entities they refer to in text. Based on the OntoNotes coreference annotation guidelines and CoNLL shared tasks. Identify mention spans and cluster coreferent mentions together.
Dialogue Relation Extraction (DialogRE)
Extract relations between entities in dialogue. Based on Yu et al., ACL 2020. Identify 36 relation types between speakers and entities mentioned in conversations.
Emotion Cause Extraction (RECCON)
Extract emotion causes from conversational text based on RECCON (Poria et al., EMNLP 2020). Identify which utterances and specific spans caused an emotion expressed in dialogue.