The automatic detection of hypernymy relationships represents a challenging problem in NLP.The successful application of stateof-the-art supervised approaches using distributed representations has generally been impeded by the limited availability of high quality training data.We have developed two novel data augmentation techniques which generate new training examples from existing ones.First, we combine the linguistic principles of hypernym transitivity and intersective modifier-noun composition to generate additional pairs of vectors, such as small dogdog or small dog -animal, for which a hypernymy relationship can be assumed.Second, we use generative adversarial networks (GANs) to generate pairs of vectors for which the hypernymy relation can also be assumed.We furthermore present two complementary strategies for extending an existing dataset by leveraging linguistic resources such as Word-Net.Using an evaluation across 3 different datasets for hypernymy detection and 2 different vector spaces, we demonstrate that both of the proposed automatic data augmentation and dataset extension strategies substantially improve classifier performance.

The interpretation of the lexical aspect of verbs in English plays a crucial role for recognizing textual entailment and learning discourse-level inferences.We show that two elementary dimensions of aspectual class, states vs. events, and telic vs. atelic events, can be modelled effectively with distributional semantics.We find that a verb's local context is most indicative of its aspectual class, and demonstrate that closed class words tend to be stronger discriminating contexts than content words.Our approach outperforms previous work on three datasets.Lastly, we contribute a dataset of human-human conversations annotated with lexical aspect and present experiments that show the correlation of telicity with genre and discourse goals.

Distributional models are derived from cooccurrences in a corpus, where only a small proportion of all possible plausible cooccurrences will be observed.This results in a very sparse vector space, requiring a mechanism for inferring missing knowledge.Most methods face this challenge in ways that render the resulting word representations uninterpretable, with the consequence that semantic composition becomes hard to model.In this paper we explore an alternative which involves explicitly inferring unobserved co-occurrences using the distributional neighbourhood.We show that distributional inference improves sparse word representations on several word similarity benchmarks and demonstrate that our model is competitive with the state-of-the-art for adjectivenoun, noun-noun and verb-object compositions while being fully interpretable.

We present a new framework for compositional distributional semantics in which the distributional contexts of lexemes are expressed in terms of anchored packed dependency trees. We show that these structures have the potential to capture the full sentential contexts of a lexeme and provide a uniform basis for the composition of distributional knowledge in a way that captures both mutual disambiguation and generalization.