@InProceedings{spiewak-sobecki-karas:2017:SemEval,
  author    = {\'{S}piewak, Martyna  and  Sobecki, Piotr  and  Kara\'{s}, Daniel},
  title     = {OPI-JSA at SemEval-2017 Task 1: Application of Ensemble learning for computing semantic textual similarity},
  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     = {139--143},
  abstract  = {Semantic Textual Similarity (STS) evaluation assesses the degree to which two
	parts of texts are similar, based on their semantic evaluation. In this paper,
	we describe three models submitted to STS SemEval 2017. Given two English parts
	of a text, each of proposed methods outputs the assessment of their semantic
	similarity.
	We propose an approach for computing monolingual semantic textual similarity
	based on an ensemble of three distinct methods. Our model consists of recursive
	neural network (RNN) text auto-encoders ensemble with supervised a model of
	vectorized sentences using reduced part of speech (PoS) weighted word
	embeddings as well as unsupervised a method based on word coverage (TakeLab).
	Additionally, we enrich our model with additional features that allow
	disambiguation of ensemble methods based on their efficiency. We have used
	Multi-Layer Perceptron as an ensemble classifier basing on estimations of
	trained Gradient Boosting Regressors.
	Results of our research proves that using such ensemble leads to a higher
	accuracy due to a fact that each member-algorithm tends to specialize in
	particular type of sentences. Simple model based on PoS weighted Word2Vec word
	embeddings seem to improve performance of more complex RNN based auto-encoders
	in the ensemble. In the monolingual English-English STS subtask our Ensemble
	based model achieved mean Pearson correlation of .785 compared with human
	annotators.},
  url       = {http://www.aclweb.org/anthology/S17-2018}
}