Intel/WEC-Eng

from huggingface_hub import hf_hub_url, cached_download
import json
REPO_ID = "datasets/Intel/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")))

{
        "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
}

@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.",
}