@InProceedings{kaushik-EtAl:2017:RANLP,
  author    = {Kaushik, Divyansh  and  Gupta, Shashank  and  Raju, Chakradhar  and  Dias, Reuben  and  Ghosh, Sanjib},
  title     = {Making Travel Smarter: Extracting Travel Information From Email Itineraries Using Named Entity Recognition},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {354--362},
  abstract  = {The purpose of this research is to address the problem of extracting
	information from travel itineraries and discuss the challenges faced in the
	process. Businessto- customer emails like booking confirmations and e-tickets
	are usually machine generated by filling slots in pre-defined
	templates which improve the presentation of such emails but also make the
	emails more complex in structure. Extracting the relevant information from
	these emails would let users track their journeys and important updates on
	applications installed on their devices to give them a consolidated over view
	of their itineraries and also save valuable time. We investigate the use of an
	HMM-based named entity recognizer on such emails which we will use to label and
	extract relevant entities. NER in such emails is challenging as these
	itineraries offer less useful contextual information. We also propose a rich
	set of features which are integrated into the model and are specific to our
	domain. The result from our model is a list of lists containing the relevant
	information extracted from ones itinerary.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_047}
}