Improving Distributional Semantic Vectors through Context Selection and

Distributional semantic models (DSMs) have been effective at representing seman-tics at the word level, and research has re-cently moved on to building distributional representations for larger segments of text. In this paper, we introduce novel ways of applying context selection and normalisa-tion to vary model sparsity and the range of values of the DSM vectors. We show how these methods enhance the quality of the vectors and thus result in improved low dimensional and composed represen-tations. We demonstrate these effects on standard word and phrase datasets, and on a new definition retrieval task and dataset