@InProceedings{kartsaklis-sadrzadeh:2016:COLING,
  author    = {Kartsaklis, Dimitri  and  Sadrzadeh, Mehrnoosh},
  title     = {Distributional Inclusion Hypothesis for Tensor-based 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     = {2849--2860},
  abstract  = {According to the distributional inclusion hypothesis, entailment between words
	can be measured via the feature inclusions of their distributional vectors. In
	recent work, we showed how this hypothesis can be extended from words to  
	phrases and sentences in the setting of compositional distributional semantics.
	This paper focuses on inclusion properties of tensors; its main contribution
	is a theoretical and experimental analysis of how feature inclusion works in
	different concrete models of verb tensors. We present results for relational,
	Frobenius,  projective, and holistic  methods and compare them to the simple
	vector addition, multiplication, min, and max models. The degrees of entailment
	thus obtained are evaluated via a variety of existing word-based measures, such
	as Weed's and Clarke's, KL-divergence, APinc, balAPinc, and two of our
	previously proposed metrics at the phrase/sentence level. We perform
	experiments on three entailment datasets, investigating which version of
	tensor-based composition achieves the highest performance when combined with
	the sentence-level measures.},
  url       = {http://aclweb.org/anthology/C16-1268}
}