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WEC-Eng

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WEC-Eng

A large-scale dataset for cross-document event coreference extracted from English Wikipedia.

Languages

English

Load Dataset

You can read in WEC-Eng files as follows (using the huggingface_hub library):

from huggingface_hub import hf_hub_url, cached_download
import json
REPO_ID = "datasets/biu-nlp/WEC-Eng"
splits_files = ["Dev_Event_gold_mentions_validated.json",
                "Test_Event_gold_mentions_validated.json",
                "Train_Event_gold_mentions.json"]
wec_eng = list()
for split_file in splits_files:
    wec_eng.append(json.load(open(cached_download(
        hf_hub_url(REPO_ID, split_file)), "r")))

Dataset Structure

Data Splits

  • Final version of the English CD event coreference dataset
    • Train - Train_Event_gold_mentions.json
    • Dev - Dev_Event_gold_mentions_validated.json
    • Test - Test_Event_gold_mentions_validated.json
Train Valid Test
Clusters 7,042 233 322
Event Mentions 40,529 1250 1,893
  • The non (within clusters) controlled version of the dataset (lexical diversity)
    • All (experimental) - All_Event_gold_mentions_unfiltered.json

Data Instances 

{
        "coref_chain": 2293469,
        "coref_link": "Family Values Tour 1998",
        "doc_id": "House of Pain",
        "mention_context": [
            "From",
            "then",
            "on",
            ",",
            "the",
            "members",
            "continued",
            "their"
  ],
  "mention_head": "Tour",
  "mention_head_lemma": "Tour",
  "mention_head_pos": "PROPN",
  "mention_id": "108172",
  "mention_index": 1,
  "mention_ner": "UNK",
  "mention_type": 8,
  "predicted_coref_chain": null,
  "sent_id": 2,
  "tokens_number": [
    50,
    51,
    52,
    53
  ],
  "tokens_str": "Family Values Tour 1998",
  "topic_id": -1
}

Data Fields

Field Value Type Value
coref_chain Numeric Coreference chain/cluster ID
coref_link String Coreference link wikipeida page/article title
doc_id String Mention page/article title
mention_context List[String] Tokenized mention paragraph (including mention)
mention_head String Mention span head token
mention_head_lemma String Mention span head token lemma
mention_head_pos String Mention span head token POS
mention_id String Mention id
mention_index Numeric Mention index in json file
mention_ner String Mention NER
tokens_number List[Numeric] Mentions tokens ids within the context
tokens_str String Mention span text
topic_id Ignore Ignore
mention_type Ignore Ignore
predicted_coref_chain Ignore Ignore
sent_id Ignore Ignore

Citation 

@inproceedings{eirew-etal-2021-wec,
    title = "{WEC}: Deriving a Large-scale Cross-document Event Coreference dataset from {W}ikipedia",
    author = "Eirew, Alon  and
      Cattan, Arie  and
      Dagan, Ido",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naacl-main.198",
    doi = "10.18653/v1/2021.naacl-main.198",
    pages = "2498--2510",
    abstract = "Cross-document event coreference resolution is a foundational task for NLP applications involving multi-text processing. However, existing corpora for this task are scarce and relatively small, while annotating only modest-size clusters of documents belonging to the same topic. To complement these resources and enhance future research, we present Wikipedia Event Coreference (WEC), an efficient methodology for gathering a large-scale dataset for cross-document event coreference from Wikipedia, where coreference links are not restricted within predefined topics. We apply this methodology to the English Wikipedia and extract our large-scale WEC-Eng dataset. Notably, our dataset creation method is generic and can be applied with relatively little effort to other Wikipedia languages. To set baseline results, we develop an algorithm that adapts components of state-of-the-art models for within-document coreference resolution to the cross-document setting. Our model is suitably efficient and outperforms previously published state-of-the-art results for the task.",
}

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/extract-wec.