Sense Embeddings

The goal of this project is to train a Continuous Bag of Words (CBOW) model using Gensim Word2Vec to create a sense embedding.

The dataset used for the training was the EuroSense dataset, which is a multilingual sense-annotated resource in 21 languages, however only the English language was used for this task.

For the correlation evaluation, the dataset WordSimilarity-353 is used.

The training was done using a Google Compute Engine instance running a Tesla K80 GPU.

Dimensionality reduction of the 40 words of the BabelNet synset with the highest number of samples

Instructions

• Generate dictionary

python preprocess.py [dictionary_name] [path] [mapping_name]

• Train

python train.py [dictionary_name]

• Score

python train.py [resource_folder] [gold_file] [model_name] [debug]

• Filter vec file to keep only BabelNet words

python convert.py [resource_folder] [vec_name] [filtered_vec_name]

• Plot PCA

python pca.py [resource_folder] [filtered_vec_name] [topnumber]