structural-correspondence-learning-SCL

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structural-correspondence-learning-SCL

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The code base for the SCL implementation used in "Neural Structural Correspondence Learning for Domain Adaptation", CoNLL 2017 and in "Pivot Based Language Modeling for Improved Neural Domain Adaptation", NAACL 2018

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# Domain Adaptation with Structural Correspondence Learning.
This is a code repository used to generate the SCL's results appearing in [Neural Structural Correspondence Learning for Domain Adaptation](https://www.aclweb.org/anthology/K/K17/K17-1040.pdf) and [Pivot Based Language Modeling for Improved Neural Domain Adaptation](http://www.aclweb.org/anthology/N18-1112).

The SCL algorithm original paper can be found [here](http://john.blitzer.com/papers/emnlp06.pdf).

If you use this implementation in your article, please cite :)
```bib
@InProceedings{ziser-reichart:2017:CoNLL,
  author    = {Ziser, Yftah  and  Reichart, Roi},
  title     = {Neural Structural Correspondence Learning for Domain Adaptation},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  year      = {2017},  
  pages     = {400--410},	
}
```
## Prerequisites
SCL requires the following packages:

Python >= 2.7

numpy

scipy

scikit-learn

## Example
You can find an explained example in run.py:

```
import tr
import sentiment
if __name__ == '__main__':
    domain = []
    domain.append("books")
    domain.append("kitchen")
    domain.append("dvd")
    domain.append("electronics")

# making a shared representation for both source domain and target domain
    # first param: the source domain
    # second param: the target domain
    # third param: number of pivots
    # fourth param: appearance threshold for pivots in source and target domain
    tr.train(domain[0],domain[1],500,10)

# learning the classifier in the source domain and testing in the target domain
    # the results, weights and all the meta-data will appear in source-target directory
    # first param: the source domain
    # second param: the target domain
    # third param: number of pivots
    # fourth param: appearance threshold for pivots in source and target domain
    # fifth param: The SVD dimension
    # sixth param: we use logistic regression as our classifier, it takes the const C for its learning
    sentiment.sent(domain[0],domain[1],500,10,50,0.1)
```

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