@InProceedings{tsujimura-miwa-sasaki:2017:SemEval,
  author    = {Tsujimura, Tomoki  and  Miwa, Makoto  and  Sasaki, Yutaka},
  title     = {TTI-COIN at SemEval-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {985--989},
  abstract  = {This paper describes our TTI-COIN system that participated in SemEval-2017 Task
	10. We investigated appropriate embeddings to adapt a neural end-to-end entity
	and relation extraction system LSTM-ER to this task. We participated in the
	full task setting of the entity segmentation, entity classification and
	relation classification (scenario 1) and the setting of relation classification
	only (scenario 3). The system was directly applied to the scenario 1 without
	modifying the codes thanks to its generality and flexibility. Our evaluation
	results show that the choice of appropriate pre-trained embeddings affected the
	performance significantly. With the best embeddings, our system was ranked
	third in the scenario 1 with the micro F1 score of 0.38. We also confirm that
	our system can produce the micro F1 score of 0.48 for the scenario 3 on the
	test data, and this score is close to the score of the 3rd ranked system in the
	task.},
  url       = {http://www.aclweb.org/anthology/S17-2172}
}