@InProceedings{agustian-takamura:2017:SemEval,
  author    = {Agustian, Surya  and  Takamura, Hiroya},
  title     = {UINSUSKA-TiTech at SemEval-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for CQA},
  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     = {370--374},
  abstract  = {The majority of core techniques to solve many problems in Community Question
	Answering (CQA) task rely on similarity computation. This work focuses on
	similarity between two sentences (or questions in subtask B) based on word
	embeddings. We exploit words importance levels in sentences or questions for
	similarity features, for classification and ranking with machine learning.
	Using only 2 types of similarity metric, our proposed method has shown
	comparable results with other complex systems. This method on subtask B 2017
	dataset is ranked on position 7 out of 13 participants. Evaluation on 2016
	dataset is on position 8 of 12, outperforms some complex systems. Further, this
	finding is explorable and potential to be used as baseline and extensible for
	many tasks in CQA and other textual similarity based system.},
  url       = {http://www.aclweb.org/anthology/S17-2061}
}