bibliography.bib

@incollection{bengio2006neural,
    title={Neural probabilistic language models},
    author={Bengio, Yoshua and Schwenk, Holger and Sen{\'e}cal, Jean-S{\'e}bastien and Morin, Fr{\'e}deric and Gauvain, Jean-Luc},
    booktitle={Innovations in Machine Learning},
    pages={137--186},
    year={2006},
    publisher={Springer},
    category={Neural Language Models}
}

@article{rumelhart1988learning,
    title={Learning representations by back-propagating errors},
    author={Rumelhart, David E and Hinton, Geoffrey E and Williams, Ronald J},
    journal={Cognitive modeling},
    volume={5},
    pages={3},
    year={1988},
    category={Neural Language Models}
}

@inproceedings{collobert2008unified,
    title={A unified architecture for natural language processing: Deep neural networks with multitask learning},
    author={Collobert, Ronan and Weston, Jason},
    booktitle={Proceedings of the 25th international conference on Machine learning},
    pages={160--167},
    year={2008},
    organization={ACM},
    category={Neural Language Models}
}

@inproceedings{mnih2009scalable,
    title={A scalable hierarchical distributed language model},
    author={Mnih, Andriy and Hinton, Geoffrey E},
    booktitle={Advances in neural information processing systems},
    pages={1081--1088},
    year={2009},
    category={Neural Language Models}
}


edings{turian2010word,
    title={Word representations: a simple and general method for semi-supervised learning},
    author={Turian, Joseph and Ratinov, Lev and Bengio, Yoshua},
    booktitle={Proceedings of the 48th annual meeting of the association for computational linguistics},
    pages={384--394},
    year={2010},
    organization={Association for Computational Linguistics},
    category={Neural Language Models}
}

@inproceedings{mikolov2013distributed,
    title={Distributed representations of words and phrases and their compositionality},
    author={Mikolov, Tomas and Sutskever, Ilya and Chen, Kai and Corrado, Greg S and Dean, Jeff},
    booktitle={Advances in neural information processing systems},
    pages={3111--3119},
    year={2013},
    category={Neural Language Models}
}

@inproceedings{mikolov2013linguistic,
    title={Linguistic Regularities in Continuous Space Word Representations.},
    author={Mikolov, Tomas and Yih, Wen-tau and Zweig, Geoffrey},
    booktitle={HLT-NAACL},
    pages={746--751},
    year={2013},
    category={Neural Language Models}
}                                                               

@article{elman1990finding,
    title={Finding structure in time},
    author={Elman, Jeffrey L},
    journal={Cognitive science},
    volume={14},
    number={2},
    pages={179--211},
    year={1990},
    publisher={Elsevier},
    category={Neural Language Models}
}

@article{mitchell2010composition,
    title={Composition in distributional models of semantics},
    author={Mitchell, Jeff and Lapata, Mirella},
    journal={Cognitive science},
    volume={34},
    number={8},
    pages={1388--1429},
    year={2010},
    publisher={Wiley Online Library},
    category={Neural Language Models}
}

@inproceedings{zanzotto2010estimating,
    title={Estimating linear models for compositional distributional semantics},
    author={Zanzotto, Fabio Massimo and Korkontzelos, Ioannis and Fallucchi, Francesca and Manandhar, Suresh},
    booktitle={Proceedings of the 23rd International Conference on Computational Linguistics},
    pages={1263--1271},
    year={2010},
    organization={Association for Computational Linguistics},
    category={Neural Language Models}
}

@inproceedings{yessenalina2011compositional,
    title={Compositional matrix-space models for sentiment analysis},
    author={Yessenalina, Ainur and Cardie, Claire},
    booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing},
    pages={172--182},
    year={2011},
    organization={Association for Computational Linguistics},
    category={Neural Language Models}
}

@article{grefenstette2013multi,
    title={Multi-step regression learning for compositional distributional semantics},
    author={Grefenstette, Edward and Dinu, Georgiana and Zhang, Yao-Zhong and Sadrzadeh, Mehrnoosh and Baroni, Marco},
    journal={arXiv preprint arXiv:1301.6939},
    year={2013},
    category={Neural Language Models}
}

@inproceedings{socher2011parsing,
    title={Parsing natural scenes and natural language with recursive neural networks},
    author={Socher, Richard and Lin, Cliff C and Manning, Chris and Ng, Andrew Y},
    booktitle={Proceedings of the 28th international conference on machine learning (ICML-11)},
    pages={129--136},
    year={2011},
    category={Neural Language Models}
}

@book{manning2008introduction,
      title={Introduction to information retrieval},
        author={Manning, Christopher D and Raghavan, Prabhakar and Schutze, Hinrich and others},
          volume={1},
            year={2008},
              publisher={Cambridge university press Cambridge},
      category={Basics},
      link={http://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf}
}

@article{mikolov2013efficient,
    title={Efficient estimation of word representations in vector space},
    author={Mikolov, Tomas and Chen, Kai and Corrado, Greg and Dean, Jeffrey},
    journal={arXiv preprint arXiv:1301.3781},
    year={2013},
    category={Neural Language Models}
}

@article{
    le2014distributed,
    title={Distributed representations of sentences and documents},
    author={Le, Quoc V and Mikolov, Tomas},
    journal={arXiv preprint arXiv:1405.4053},
    year={2014},
    category={Neural Language Models}
}

@article{deng2014association,
    title={Association pattern discovery via theme dictionary models},
    author={Deng, Ke and Geng, Zhi and Liu, Jun S},
    journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
    volume={76},
    number={2},
    pages={319--347},
    year={2014},
    publisher={Wiley Online Library},
    category={Topic Models}
}

@article{grimmer2010bayesian,
      title={A Bayesian hierarchical topic model for political texts: Measuring expressed agendas in Senate press releases},
        author={Grimmer, Justin},
          journal={Political Analysis},
            volume={18},
              number={1},
                pages={1--35},
                  year={2010},
                    publisher={SPM-PMSAPSA},
      category={Topic Models}
}

@article{roberts2014structural,
    title={Structural Topic Models for Open-Ended Survey Responses},
    author={Roberts, Margaret E and Stewart, Brandon M and Tingley, Dustin and Lucas, Christopher and Leder-Luis, Jetson and Gadarian, Shana Kushner and Albertson, Bethany and Rand, David G},
    journal={American Journal of Political Science},
    volume={58},
    number={4},
    pages={1064--1082},
    year={2014},
    publisher={Wiley Online Library},
    category={Topic Models}
}

@inproceedings{ramage2009labeled,
    title={Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora},
    author={Ramage, Daniel and Hall, David and Nallapati, Ramesh and Manning, Christopher D},
    booktitle={Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1},
    pages={248--256},
    year={2009},
    organization={Association for Computational Linguistics},
    category={Topic Models}
}

@article{blei2003latent,
    title={Latent dirichlet allocation},
    author={Blei, David M and Ng, Andrew Y and Jordan, Michael I},
    journal={the Journal of machine Learning research},
    volume={3},
    pages={993--1022},
    year={2003},
    publisher={JMLR. org},
    category={Topic Models}
}

@article{blei2012probabilistic,
    title={Probabilistic topic models},
    author={Blei, David M},
    journal={Communications of the ACM},
    volume={55},
    number={4},
    pages={77--84},
    year={2012},
    publisher={ACM},
    category={Topic Models}   
}