DEFT: A corpus for definition extraction in free- and semi-structured text
By Sasha Spala, Nicholas A. Miller, Yiming Yang, Franck Dernoncourt, Carl Dockhorn. 2019
45 citations recorded by the source corpus.
Definition extraction has been a popular topic in NLP research for well more than a decade, but has been historically limited to well-defined, structured, and narrow conditions. In reality, natural language is messy, and messy data requires both complex solutions and data that reflects that reality. In this paper, we present a robust English corpus and annotation schema that allows us to explore the less straightforward examples of term-definition structures in free and semi-structured text.
Subfields: Artificial Intelligence, Information Extraction
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Citations in the corpus
- Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections (2021, neutral): “2https://www.kaggle.com 3https://semeval.github.io 4https://www.kaggle.com/c/ detection (SemEval 2020 Task 6, Spala et al. (2019)), stance classification (SemEval 2016 Task 6, Mohammad et al. (2016)), etc.”
- SemEval-2020 Task 6: Definition Extraction from Free Text with the DEFT Corpus (2020, background): “Because of the mixed nature of the source problem for tokenization errors, many caused incorrect sentence tokenization, which in turn meant that the actual number of sentences in the corpus was different from the reported number in (Spala et al., 2019).”