Department of Linguistics, Stockholm University and Gavagai, Stockholm
This chapter deals with a statistical technique for sense exploration based on distributional semantics known as word space modelling. Word space models rely on feature aggregation, in this case aggregation of co-occurrence events, to build an aggregated view on the distributional behaviour of words. Such models calculate meaning similarity among words on the basis of the contexts in which they occur and represent it as proximity in high-dimensional vector spaces. The main purpose of this study is to test to what extent word-space modelling is in principle suitable for lexical-typological work by taking a first little step in this direction and applying the method for the exploration of the seven central English temperature adjectives in three corpora representing different genres. In order to better capture and account for the potentially different senses of one and the same word we have suggested and applied a new variant of this general method, “syntagmatically labelled partitioning”.
In: Benedikt Szmrecsanyi and Bernhard Wälchli (eds.) Aggregating Dialectology, Typology, and Register Analysis: Linguistic Variation in Text and Speech, Berlin, Boston: De Gruyter, 2014, pp. 231–267.