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README.md
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The model is trained as a binary classifier, classifying candidate nominalizations.
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The candidates are extracted using a POS tagger (filtering common nouns) and additionally lexical resources (e.g. WordNet and CatVar), filtering nouns that have (at least one) derivationally-related verb. In the QANom annotation project, these candidates are given to annotators to decide whether they carry a "verbal" meaning in the context of the sentence. The current model reproduces this binary classification.
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## Usage
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The candidate extraction algorithm is implemented inside the `qanom` package - see the README in the [QANom github repo](https://github.com/kleinay/QANom) for full documentation. The `qanom` package is also available via `pip install qanom`.
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The model is trained as a binary classifier, classifying candidate nominalizations.
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The candidates are extracted using a POS tagger (filtering common nouns) and additionally lexical resources (e.g. WordNet and CatVar), filtering nouns that have (at least one) derivationally-related verb. In the QANom annotation project, these candidates are given to annotators to decide whether they carry a "verbal" meaning in the context of the sentence. The current model reproduces this binary classification.
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## Demo
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Check out our cool [demo](https://huggingface.co/spaces/kleinay/nominalization-detection-demo)!
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## Usage
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The candidate extraction algorithm is implemented inside the `qanom` package - see the README in the [QANom github repo](https://github.com/kleinay/QANom) for full documentation. The `qanom` package is also available via `pip install qanom`.
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