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cometa's Introduction

COMETA: the corpus of online medical entities

This repo holds code for running baseline models presented in our paper: COMETA: A Corpus for Medical Entity Linking in the Social Media at EMNLP 2020.

COMETA is an entity linking dataset of layman medical terminology. It has been collected by analysing four years of content in 68 health-themed subreddits and annotating the most frequent with their corresponding SNOMED-CT entities. Each term is assigned two annotations: a General SNOMED-CT identifier and a Specific one, denoting respectively the literal and contextual meaning of the term.

For a copy of the corpus, please download here.

Pretrained vectors

Model Download Link
Bioreddit-FastText bin, vec
Bioreddit-BERT huggingface

You can find vectors trained on the same Bioreddit corpus for ELMo, Flair and GloVE in this repository.

Citation

If you use our corpus or our embeddings, please cite:

@inproceedings{basaldella-etal-2020-cometa,
    title = "{COMETA}: A Corpus for Medical Entity Linking in the Social Media",
    author = "Basaldella, Marco  and
      Liu, Fangyu  and
      Shareghi, Ehsan  and
      Collier, Nigel",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.253",
    pages = "3122--3137",
}

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cometa's Issues

License information

Thank you so much for the great work. Can you specify the license for using the data, please?

Reproducing Stoilos Distance Based Result

Hi,

I am trying to reproduce your results on zero shot general dataset using Stoilos distance. I have used your pre-computed file term_onto_ed_dic_zeroshot_general.pkl. I took the first element with the smallest distance and counted as a hit if it was either match with the entity or its synonyms, but I am only getting accuracy of 0.3011.

Please let me know what you think. Thank you!

count = 0
corr = 0
for g in gold_entites_dict:
    if mid_to_idx[g] not in ONT_ED_DICT: continue
    count += 1
    label_id = label_ids[g]
    for term in dp.snomed.index_definition[str(dp.label_to_snomed_id[label_id])]:
        cur_result = int(term.lower() == ONT_ED_DICT[mid_to_idx[g]][0][0].lower())
        if cur_result == 1:
            corr += 1
            break

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