This code implements NNQLM2 in the paper: End-to-End Quantum-like Language Models with Application to Question Answering. AAAI2018 # DEPENDENCIES - python 2.7+ - numpy - theano - scikit-learn (sklearn) - ConfigParser - cPickle - pandas - os - sys - time Python packages can be easily installed using the standard tool: pip install <package> # RUN You can run this model by: >$ python run.py This model is for trecqa and wikiqa. The default is for trecqa, and if you want to run this model on wikiqa, you can: >$ python run.py wiki You can use other qa dataset. Please put your dataset on dir dataset, and preprocess your data according to trecqa. # REFERENCES Aliaksei Severyn and Alessandro Moschitti. Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks. SIGIR, 2015. Sordoni A, Nie J Y, Bengio Y. Modeling term dependencies with quantum language models for IR. SIGIR, 2013. Kim Y. Convolutional Neural Networks for Sentence Classification. Eprint Arxiv, 2014.