The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 4 new columns ({'bm25_query', 'positive_examples', 'answers', 'negative_examples'}) and 4 missing columns ({'offsets_in_document', 'answer', 'remove', 'score'}).
This happened while the json dataset builder was generating data using
hf://datasets/biu-nlp/CoreSearchV2/train/Dev_queries_min_span.json (at revision 18c49d53fcf0c04e36543a434b323bfd5245d526)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
goldChain: string
negative_examples: list<item: string>
child 0, item: string
bm25_query: string
answers: null
endIndex: int64
context: list<item: string>
child 0, item: string
id: string
positive_examples: list<item: string>
child 0, item: string
startIndex: int64
mention: list<item: string>
child 0, item: string
to
{'answer': Value(dtype='null', id=None), 'goldChain': Value(dtype='string', id=None), 'endIndex': Value(dtype='int64', id=None), 'context': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'id': Value(dtype='string', id=None), 'score': Value(dtype='float64', id=None), 'offsets_in_document': Value(dtype='null', id=None), 'startIndex': Value(dtype='int64', id=None), 'remove': Value(dtype='bool', id=None), 'mention': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 4 new columns ({'bm25_query', 'positive_examples', 'answers', 'negative_examples'}) and 4 missing columns ({'offsets_in_document', 'answer', 'remove', 'score'}).
This happened while the json dataset builder was generating data using
hf://datasets/biu-nlp/CoreSearchV2/train/Dev_queries_min_span.json (at revision 18c49d53fcf0c04e36543a434b323bfd5245d526)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
goldChain string | answer null | startIndex int64 | endIndex int64 | score float64 | offsets_in_document null | remove null | id string | mention sequence | context sequence |
|---|---|---|---|---|---|---|---|---|---|
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The CoreSearch Dataset
A cleaner version of the CoreSearch dataset
Cleaning done in this version
Below examples of fixes done in this dataset version
Fix event mention spans
| Before Span Fix | After Span Fix |
|---|---|
| Academy Awards 65th ceremony | Academy Awards |
| 65th Annual Academy Awards | Annual Academy Awards |
| 2015 Southland Conference Women's Basketball Tournament | Southland Conference Women's Basketball Tournament |
| 2010 Austin suicide attack | attack |
| an earthquake in 1948 | earthquake |
| tragic plane crash | crash |
| Lokomotiv Yaroslavl plane tragedy | tragedy |
| just been shot | shot |
| wounded by gunfire | wounded |
| Kennedy's assassination in Los Angeles | assassination |
| Arab League summit in Cairo | summit |
| plane crash on 4 July 1943 | crash |
Resolve cases of two events in a single span
| Before Span Fix | After Span Fix |
|---|---|
| killing or abduction of several hundred villagers | killing |
| kidnapping and murder of Aldo Moro | kidnapping |
Remove erroneous event mentions (Entities)
| Mention Removed | Reason (not event) |
|---|---|
| I Want Your Love | Song name |
| 2006 Screen Actors Guild Award for Best Ensemble | Award name |
| in 1896 | Year |
Languages
English
Load Dataset
You can read/download the dataset files following Huggingface Hub instructions.
For example, below code will load CoreSearch DPR folder:
from huggingface_hub import hf_hub_url, cached_download
import json
REPO_ID = "datasets/Intel/CoreSearchV2"
DPR_FILES = "/dpr/"
dpr_files = ["dpr/Dev.json", "dpr/Train.json", "dpr/Test.json"]
dpr_jsons = list()
for _file in dpr_files:
dpr_jsons.append(json.load(open(cached_download(
hf_hub_url(REPO_ID, _file)), "r")))
Data Splits
- Final version of the CD event coreference search dataset
| | Train | Valid | Test | Total |
| ----- | ------ | ----- | ---- | ---- | | WEC-Eng Validated Data | | | | | | # Clusters | 229 | 48 | 226 | 503 | | # Passages (with Mentions) | 1,429 | 335 | 1,206 | 2,970 | | # Added Destructor Passages | 922,736 | 923,376 | 923,746 | 2,769,858 | | # Total Passages | 924,239 | 923,717 | 925,012 | 2,772,968 |
Citation
@inproceedings{eirew-etal-2022-cross,
title = "Cross-document Event Coreference Search: Task, Dataset and Modeling",
author = "Eirew, Alon and
Caciularu, Avi and
Dagan, Ido",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.58",
pages = "900--913",
abstract = "The task of Cross-document Coreference Resolution has been traditionally formulated as requiring to identify all coreference links across a given set of documents. We propose an appealing, and often more applicable, complementary set up for the task {--} Cross-document Coreference Search, focusing in this paper on event coreference. Concretely, given a mention in context of an event of interest, considered as a query, the task is to find all coreferring mentions for the query event in a large document collection. To support research on this task, we create a corresponding dataset, which is derived from Wikipedia while leveraging annotations in the available Wikipedia Event Coreferecene dataset (WEC-Eng). Observing that the coreference search setup is largely analogous to the setting of Open Domain Question Answering, we adapt the prominent Deep Passage Retrieval (DPR) model to our setting, as an appealing baseline. Finally, we present a novel model that integrates a powerful coreference scoring scheme into the DPR architecture, yielding improved performance.",
}
License
We provide the following data sets under a Creative Commons Attribution-ShareAlike 3.0 Unported License. It is based on content extracted from Wikipedia that is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License
Contact
If you have any questions please create a Github issue at https://github.com/AlonEirew/CoreSearch.
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