@InProceedings{panchenko-EtAl:2017:EMNLP2017Demos,
  author    = {Panchenko, Alexander  and  Marten, Fide  and  Ruppert, Eugen  and  Faralli, Stefano  and  Ustalov, Dmitry  and  Ponzetto, Simone Paolo  and  Biemann, Chris},
  title     = {Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {91--96},
  abstract  = {Interpretability of a predictive model is a powerful feature that gains the
	trust of users in the correctness of the predictions.  In word sense
	disambiguation (WSD), knowledge-based systems tend to be much more
	interpretable than knowledge-free counterparts as they rely on the wealth of
	manually-encoded elements representing word senses, such as hypernyms, usage
	examples, and images. We present a WSD system that bridges the gap between
	these two so far disconnected groups of methods. Namely, our system, providing
	access to several state-of-the-art WSD models, aims to be interpretable as a
	knowledge-based system while it remains completely unsupervised and
	knowledge-free. The presented tool features a Web interface for all-word
	disambiguation of texts that makes the sense predictions human readable by
	providing interpretable word sense inventories, sense representations, and
	disambiguation results. We provide a public API, enabling seamless integration.},
  url       = {http://www.aclweb.org/anthology/D17-2016}
}