@InProceedings{nguyen-schulteimwalde-vu:2017:EACLlong,
  author    = {Nguyen, Kim Anh  and  Schulte im Walde, Sabine  and  Vu, Ngoc Thang},
  title     = {Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {76--85},
  abstract  = {Distinguishing between antonyms and synonyms is a key task to achieve high
	performance in NLP systems. While they are notoriously difficult to distinguish
	by distributional co-occurrence models, pattern-based methods have proven
	effective to differentiate between the relations. In this paper, we present a
	novel neural network model AntSynNET that exploits lexico-syntactic patterns
	from syntactic parse trees. In addition to the lexical and syntactic
	information, we successfully integrate the distance between the related words
	along the syntactic path as a new pattern feature. The results from
	classification experiments show that AntSynNET improves the performance over
	prior pattern-based methods.},
  url       = {http://www.aclweb.org/anthology/E17-1008}
}