readme: ---
  language:
  - en
  tags:
  - pytorch
  - token-classification
  - nominalizations
  datasets:
  - kleinay/qanom
  ---
  
  # Nominalization Detector
  
  This model identifies "predicative nominalizations", that is, nominalizations that carry an eventive (or "verbal") meaning in context. It is a `bert-base-cased` pretrained model, fine-tuned for token classification on top of the "nominalization detection" task as defined and annotated by the QANom project [(Klein et. al., COLING 2020)](https://www.aclweb.org/anthology/2020.coling-main.274/).
  
  ## Task Description
  
  The model is trained as a binary classifier, classifying candidate nominalizations. 
  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. 
  
  ## Demo
  
  Check out our cool [demo](https://huggingface.co/spaces/kleinay/nominalization-detection-demo)!
  
  ## Usage
  
  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`. 
  
  For ease of use, we encapsulated the full nominalization detection pipeline (i.e. candidate extraction + predicate classification) in the `qanom.nominalization_detector.NominalizationDetector` class, which internally utilize this `nominalization-candidate-classifier`:
  
   ```python
  from qanom.nominalization_detector import NominalizationDetector
  detector = NominalizationDetector()
   
  raw_sentences = ["The construction of the officer 's building finished right after the beginning of the destruction of the previous construction ."]
  
  print(detector(raw_sentences, return_all_candidates=True))
  print(detector(raw_sentences, threshold=0.75, return_probability=False))
  ```   
  
  Outputs:
  ```json
  [[{'predicate_idx': 1,
     'predicate': 'construction',
     'predicate_detector_prediction': True,
     'predicate_detector_probability': 0.7626778483390808,
     'verb_form': 'construct'},
    {'predicate_idx': 4,
     'predicate': 'officer',
     'predicate_detector_prediction': False,
     'predicate_detector_probability': 0.19832570850849152,
     'verb_form': 'officer'},
    {'predicate_idx': 6,
     'predicate': 'building',
     'predicate_detector_prediction': True,
     'predicate_detector_probability': 0.5794129371643066,
     'verb_form': 'build'},
    {'predicate_idx': 11,
     'predicate': 'beginning',
     'predicate_detector_prediction': True,
     'predicate_detector_probability': 0.8937646150588989,
     'verb_form': 'begin'},
    {'predicate_idx': 14,
     'predicate': 'destruction',
     'predicate_detector_prediction': True,
     'predicate_detector_probability': 0.8501205444335938,
     'verb_form': 'destruct'},
    {'predicate_idx': 18,
     'predicate': 'construction',
     'predicate_detector_prediction': True,
     'predicate_detector_probability': 0.7022264003753662,
     'verb_form': 'construct'}]]
  ```
  ```json
  [[{'predicate_idx': 1, 'predicate': 'construction', 'verb_form': 'construct'},
    {'predicate_idx': 11, 'predicate': 'beginning', 'verb_form': 'begin'},
    {'predicate_idx': 14, 'predicate': 'destruction', 'verb_form': 'destruct'}]]
  ```
    
  ## Cite
  
   ```latex
   @inproceedings{klein2020qanom,
    title={QANom: Question-Answer driven SRL for Nominalizations},
    author={Klein, Ayal and Mamou, Jonathan and Pyatkin, Valentina and Stepanov, Daniela and He, Hangfeng and Roth, Dan and Zettlemoyer, Luke and Dagan, Ido},
    booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
    pages={3069--3083},
    year={2020}
  }
   ``` 
  