@InProceedings{wang-mi-ittycheriah:2016:COLING,
  author    = {Wang, Zhiguo  and  Mi, Haitao  and  Ittycheriah, Abraham},
  title     = {Sentence Similarity Learning by Lexical Decomposition and Composition},
  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     = {1340--1349},
  abstract  = {Most conventional sentence similarity methods only focus on similar parts of
	two input sentences,
	and simply ignore the dissimilar parts, 
	which usually give us some clues and semantic meanings about the sentences. 
	In this work, we propose a model to take into account both the similarities and
	dissimilarities 
	by decomposing and composing lexical semantics over sentences. 
	The model represents each word as a vector, 
	and calculates a semantic matching vector for each word based on all words in
	the other sentence. 
	Then, each word vector is decomposed into a similar component and a dissimilar
	component based on the semantic matching vector. 
	After this, a two-channel CNN model is employed to capture features 
	by composing the similar and dissimilar components. 
	Finally, a similarity score is estimated over the composed feature vectors. 
	Experimental results 
	show that 
	our model gets the state-of-the-art performance on the answer sentence
	selection task, 
	and achieves a comparable result on the paraphrase identification task.},
  url       = {http://aclweb.org/anthology/C16-1127}
}