@InProceedings{panchenko-EtAl:2017:EACLlong,
  author    = {Panchenko, Alexander  and  Ruppert, Eugen  and  Faralli, Stefano  and  Ponzetto, Simone Paolo  and  Biemann, Chris},
  title     = {Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {86--98},
  abstract  = {The current trend in NLP is the use of highly opaque models, e.g. neural
	networks and word embeddings. While these models yield state-of-the-art results
	on a range of tasks, their drawback is poor interpretability. On the example of
	word sense induction and disambiguation (WSID), we show that it is possible to
	develop an interpretable model that matches the state-of-the-art models in
	accuracy. Namely, we present an unsupervised, knowledge-free WSID approach,
	which is interpretable at three levels: word sense inventory, sense feature
	representations, and disambiguation procedure. Experiments show that our model
	performs on par with state-of-the-art word sense embeddings and other
	unsupervised systems while offering the possibility to justify its decisions in
	human-readable form.},
  url       = {http://www.aclweb.org/anthology/E17-1009}
}