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FrancophonIA/MEmoLon

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Permissive or share-alike

The declared licence (cc-by-4.0) is classified as permissive.

Declared licence: cc-by-4.0, from the tagEvidence: README.md at 66eef8a
Licence files: No licence file at the top level of the repositoryEvidence: file list at 66eef8a
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Tasks

text-classification

Citation text from the card

@inproceedings{buechel-etal-2020-learning-evaluating,
    title = "Learning and Evaluating Emotion Lexicons for 91 Languages",
    author = {Buechel, Sven  and
      R{\"u}cker, Susanna  and
      Hahn, Udo},
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.112/",
    doi = "10.18653/v1/2020.acl-main.112",
    pages = "1202--1217",
    abstract = "Emotion lexicons describe the affective meaning of words and thus constitute a centerpiece for advanced sentiment and emotion analysis. Yet, manually curated lexicons are only available for a handful of languages, leaving most languages of the world without such a precious resource for downstream applications. Even worse, their coverage is often limited both in terms of the lexical units they contain and the emotional variables they feature. In order to break this bottleneck, we here introduce a methodology for creating almost arbitrarily large emotion lexicons for any target language. Our approach requires nothing but a source language emotion lexicon, a bilingual word translation model, and a target language embedding model. Fulfilling these requirements for 91 languages, we are able to generate rep

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dataset_licence({ "id": "FrancophonIA/MEmoLon" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0