@inproceedings{Rudolph:2010:CMM:1858681.1858774,
  abstract = {We propose CMSMs, a novel type of generic compositional models for syntactic and semantic aspects of natural language, based on matrix multiplication. We argue for the structural and cognitive plausibility of this model and show that it is able to cover and combine various common compositional NLP approaches ranging from statistical word space models to symbolic grammar formalisms.},
  acmid = {1858774},
  address = {Stroudsburg, PA, USA},
  author = {Rudolph, Sebastian and Giesbrecht, Eugenie},
  booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics},
  interhash = {6594500d38a361829aeb3ef7889a1709},
  intrahash = {05ec57c39e9b945deb674c3b616eac8f},
  location = {Uppsala, Sweden},
  numpages = {10},
  pages = {907--916},
  publisher = {Association for Computational Linguistics},
  series = {ACL '10},
  title = {Compositional matrix-space models of language},
  url = {http://dl.acm.org/citation.cfm?id=1858681.1858774},
  year = 2010
}

@article{mitzenmacher2004history,
  abstract = {Recently, I became interested in a current debate over whether file size distributions are best modelled by a power law distribution or a lognormal distribution. In trying to learn enough about these distributions to settle the question, I found a rich and long history, spanning many fields. Indeed, several recently proposed models from the computer science community have antecedents in work from decades ago. Here, I briefly survey some of this history, focusing on underlying generative models that
lead to these distributions. One finding is that lognormal and power law distributions connect quite naturally, and hence, it is not surprising that lognormal distributions have arisen as a possible alternative to power law distributions across many fields.
},
  author = {Mitzenmacher, M.},
  interhash = {50b0caa36c6cbc1ecfa0714157f06bd1},
  intrahash = {acdeb6b7980b25477665939c191f1e40},
  journal = {Internet Mathematics},
  number = 2,
  pages = {226--251},
  title = {A Brief History of Generative Models for Power Law and Lognormal Distributions
},
  url = {http://www.eecs.harvard.edu/~michaelm/CS223/powerlaw.pdf},
  volume = 1,
  year = 2004
}