@InProceedings{song-EtAl:2016:COLING2,
  author    = {Song, Wei  and  Fu, Ruiji  and  Liu, Lizhen  and  Wang, Hanshi  and  Liu, Ting},
  title     = {Anecdote Recognition and Recommendation},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2592--2602},
  abstract  = {We introduce a novel task Anecdote Recognition and Recommendation. An anecdote
	is a story with a point revealing account of an individual person. Recommending
	proper anecdotes can be used as evidence to support argumentative writing or as
	a clue for further reading.
	We represent an anecdote as a structured tuple — < person, story, implication
	>. Anecdote recognition runs on archived argumentative essays. We extract
	narratives containing events of a person as the anecdote story. More
	importantly, we uncover the anecdote implication, which reveals the meaning and
	topic of an anecdote. Our approach depends on discourse role identification.
	Discourse roles such as thesis, main ideas and support help us locate stories
	and their implications in essays. The experiments show that informative and
	interpretable anecdotes can be recognized. These anecdotes are used for
	anecdote recommendation. The anecdote recommender can recommend proper
	anecdotes in response to given topics. The anecdote implication contributes
	most for bridging user interested topics and relevant anecdotes.},
  url       = {http://aclweb.org/anthology/C16-1244}
}