frankl1/Word2vec-For-NER-In-Low-Resource-Languages

An efficient word representation for named entities recognition in low-resource languages. # Word2vec-For-NER-In-Low-Resource-Languages An efficient language-free distributionnal representation for the projection of named entities from a rich language to low-resource languages. Checkout the paper for more details # Process flow diagram # Repository description - The `data` folder contains the annotations of the corpus using Brat. - The `result` folder contains the results (F1, Precision, Recall) by tag for the 10 repetitions of the training. The file (results.csv)[github.com] constains the averaged results for the 10 repetitions. - The jupyter notebook brat2corpus.ipynb is used to convert brat annotation to two parallel copora corpus-ewo.txt (for the Ewondo side) and corpus-en.txt (for the English side). Each line in these file is a token with its associated annotation. Blank lines denote the end of a phrase (verse in our case). The two files are aligned such that a phrase and its translation are at the same position. - The jupyter notebooks fnn-ne-projection-corpus-based-tf.ipynb, fnn-ne-projection-phrase-based.ipynb, and fnn-ne-projection-side-based.ipynb are the source codes used to train the model and compute performances using respectively the *corpus-based term frequency*, the *phrase-based term frequency*, and the *side-based term frequency*. # Reproduce the experiment After cloning this repository, `conda` can be used to install all the dependencies with the command `conda env create -f requirements.yml` # Citing this work ~~~ @article{mbouopda2020named, title={Named Entity Recognition in Low-resource Languages using Cross-lingual distributional word representation}, author={Mbouopda, Michael Franklin and Melatagia Yonta, Paulin}, journal={Revue Africaine de la Recherche en Informatique et Math{\'e}matiques Appliqu{\'e}es}, volume={33}, year={2020}, publisher={Episciences. org} }