nikhilsu / CitationRecommender

Project to recommend Citations to a research paper

Citation Recommender

Project to recommend Citations given a document. The scope of the project is to replicate the research done as part of Content-Based Citation Recommendation paper.

Credits: citeomatic (GitHub)

Project Dependencies

• The project dependencies(python libraries) can be installed by running the following command:-

$ pip install -r requirements.txt

Order of Training Models

Train word embeddings

• You will need access the dataset which is sits inside a MongoDB instance hosted on the GCP. (Please contact the owner/s of the repo for the credential or the db url.)
• Once you get access to the MongoDB credentials, please export these credentials in to your systems environment variables.
• Now that that is done, run the below commands to start training your Word Embeddings network.

$ export PYTHONPATH="$PYTHONPATH:$(pwd)"
$ python train.py --model embeddings

Train NN_Rank model

• NNRank Model depends on the word_embedding model's and the dense model's weight.
• Thus, you would need to pre-train the word_embeddings model before you train the NNRank.
• The embedding and the dense weights are found in the weights directory once the training of the Word embeddings model is complete.
• Run the below commands to start training your NN_Rank network.

$ export PYTHONPATH="$PYTHONPATH:$(pwd)"
$ python train.py --model NNRank --embeddings_model_weights_path "<path_to_embeddings_model_weight>" --dense_model_weights_path "<path_to_dense_model_weights>"

Testing Model

Testing NN_Select Model

• NN_Select or the Candidate Selector model can be tested by utilizing any of word_embedding_model weights in the weights directory. (Note: These model weight files will be generated on training the Word Embeddings model)
• Run the following command to test the model

$ python test.py --model NNSelect --embeddings_model_weights_path "<path_to_weights>"

Testing NN_Rank Model

• NN_Rank model can be tested by utilizing nn_rank_weights in the weights directory.
• Run the following command to test the model
• This model is evaluated based on various Metrics like:-
  1. Precision
  2. Accuracy
  3. Mean Reciprocal Rank
  4. Recall
  5. F1 measure

$ python test.py --model NNRank --nn_rank_model_weights_path "<path_to_nn_rank_weights>"