
# 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}
}
 ``` 

{"language": ["en"], "tags": ["pytorch", "token-classification", "nominalizations"], "datasets": ["kleinay/qanom"]}
transformers pytorch bert token-classification nominalizations en dataset:kleinay/qanom autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #token-classification #nominalizations #en #dataset-kleinay/qanom #autotrain_compatible #endpoints_compatible #has_space #region-us 


# 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).

## 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!

## Usage

The candidate extraction algorithm is implemented inside the 'qanom' package - see the README in the QANom github repo 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':

    

Outputs:


  
## Cite

  

# 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). ## 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! ## Usage

The candidate extraction algorithm is implemented inside the 'qanom' package - see the README in the QANom github repo 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':

    

Outputs: ## Cite
TAGS
#transformers #pytorch #bert #token-classification #nominalizations #en #dataset-kleinay/qanom #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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). ## 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! ## Usage

The candidate extraction algorithm is implemented inside the 'qanom' package - see the README in the QANom github repo 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':

    

Outputs: ## Cite
passage: TAGS
#transformers #pytorch #bert #token-classification #nominalizations #en #dataset-kleinay/qanom #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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).## 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!## Usage

The candidate extraction algorithm is implemented inside the 'qanom' package - see the README in the QANom github repo 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':

    

Outputs:## Cite