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staneeya
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EASIE

Tasks:

Text Classification

Languages:

Thai

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1K<n<10K

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The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<Artifact-Arg: string, Owner-Arg: string, Occupant-Arg: string, Period-Arg: string, StartDate-Arg: string, Intention-Arg: string, Landlord-Arg: string, Tenant-Arg: string, Time-Arg: string, Time-Arg2: string>
to
{'Giver-Arg': Value('string'), 'Receiver-Arg': Value('string'), 'Time-Arg': Value('string'), 'Owner-Arg': Value('string'), 'Artifact-Arg': Value('string'), 'Occupant-Arg2': Value('string'), 'Intention-Arg': Value('string'), 'Period-Arg': Value('string'), 'Occupant-Arg': Value('string'), 'Artifact-Arg2': Value('string'), 'Owner-Arg2': Value('string'), 'Receiver-Arg2': Value('string'), 'StartDate-Arg': Value('string'), 'Occupant-Arg3': Value('string')}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2315, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2121, in cast_array_to_feature
                  casted_array_values = _c(array.values, feature.feature)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2071, in cast_array_to_feature
                  _c(children[name] if name in array_fields else null_array, subfeature)
                  ~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2161, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<Artifact-Arg: string, Owner-Arg: string, Occupant-Arg: string, Period-Arg: string, StartDate-Arg: string, Intention-Arg: string, Landlord-Arg: string, Tenant-Arg: string, Time-Arg: string, Time-Arg2: string>
              to
              {'Giver-Arg': Value('string'), 'Receiver-Arg': Value('string'), 'Time-Arg': Value('string'), 'Owner-Arg': Value('string'), 'Artifact-Arg': Value('string'), 'Occupant-Arg2': Value('string'), 'Intention-Arg': Value('string'), 'Period-Arg': Value('string'), 'Occupant-Arg': Value('string'), 'Artifact-Arg2': Value('string'), 'Owner-Arg2': Value('string'), 'Receiver-Arg2': Value('string'), 'StartDate-Arg': Value('string'), 'Occupant-Arg3': Value('string')}

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EASIE: Event Annotation for Supreme-court Incident Extraction in Thai

Dataset Description

This dataset contains event annotations for Thai Supreme Court judgments. It is designed to support research on event extraction and structured information extraction from Thai legal texts.

The dataset consists of 875 judgments, with 125 judgments for each of seven selected legal sections. It contains 14 event types, 35 event-associated semantic roles, and annotations for event attributes and properties.

Data Source

The judgments were collected from the DEKA Supreme Court Judgment Database using searches based on legal-section numbers.

The dataset covers the following sections:

Section	Legal code	Subject
59	Criminal Code	Intent and negligence
80	Criminal Code	Attempt to commit an offence
83	Criminal Code	Joint commission of an offence
288	Criminal Code	Homicide
420	Civil and Commercial Code	Tort liability
537	Civil and Commercial Code	Lease
1382	Civil and Commercial Code	Adverse possession

Annotation

The dataset uses a trigger–argument approach. Each event is annotated with a trigger and its associated semantic roles. Event attributes and properties capture additional information, including negation, failure, and injury severity.

The 14 event types are:

Attack, Arrest, Cooperate, Communicate, Die, Evict, Infringe, Liable, Occupy, Operate, Lease, Transfer, Injure, and Robbery.

Three annotators performed the annotation following training. Final annotations were determined through majority agreement. The reported inter-annotator agreement was Krippendorff's alpha = 0.85.

Annotation begins at a predefined Supreme Court deliberation cue phrase and continues to the end of each judgment. The preceding text is retained but excluded from annotation.

Dataset Statistics

Statistic	Value
Judgments	875
Event types	14
Event instances	21,287
Semantic role instances	37,737

Dataset Organization

Documents are organized by types of contents: BRAT and JSON. Each folder contains 7 section numbers as subfolders.

dataset/
├── BRAT/
│   ├── 59/
|   | 	  ├─2525_1206.ann
|   | 	  ├─2525_1206.txt
├── |	  └── ...
├── ├── 80/
├── ├── 83/
├── ├── 288/
├── ├── 420/
├── ├── 537/
├── ├── 1382/
|   |
└── JSON/
│   ├── 59/
|   | 	  ├─2525_1206.json
|   | 	  └── ...
├── ├── 80/
├── ├── 83/
├── ├── 288/
├── ├── 420/
├── ├── 537/
└── └── 1382/

BRAT: .txt files contain judgment text; .ann files contain stand-off annotations.
JSON: Contains the judgment text and structured entities, relations, events, and attributes.

Intended Uses

The dataset supports research on Thai legal event extraction, semantic role labeling, legal text mining, structured legal knowledge representation, and law-section prediction.

Limitations

The dataset covers only seven selected legal sections and uses a restricted annotation scope within each judgment. Annotation consistency and the complexity of legal language should be considered when using the data for model training and evaluation.

Data Access and License

Source judgments were obtained from the publicly accessible DEKA Supreme Court Judgment Database. Selected identifying metadata were removed during preprocessing. Users should verify applicable source-data terms and legal requirements before redistributing or reusing the judgment texts.

License: cc-by-4.0.

Citation

If you use this dataset, please cite:

[Taneeya Satyapanich]. ([2026]). EASIE: Event Annotation for Supreme-court Incident Extraction in Thai. [Repository URL or DOI].

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