Generation of linguistic membership functions from word vectors
Thomas A. Runkler · 2016
Our goal is to automatically generate membership functions for linguistic terms such as newborn, baby, child, teen, and adult. Many approaches have been proposed to generate membership functions from data, for example using fuzzy clustering or neural networks, but these generate abstract membership functions with no semantic meaning. Our approach uses word vectors that are extracted from large text corpora, so that semantically similar words have similar word vectors. We use projections of word vectors to (i) generate the peaks of membership functions over one-dimensional domains and use similarities between pairs of word vectors to (ii) compute selected membership values. We present four different alternatives to construct membership functions from (i) and (ii), which produce (a) singleton functions, (b) piecewise linear functions, (c) triangular functions, and (d) normalized triangular functions. In the experimental part we present examples from four different domains where our method successfully generates linguistically meaningful membership functions from word vectors of the 50-dimensional glove.6B.50d data set, and we also present an example that illustrates the limitations of the word vector based approach, when the considered linguistic terms come from different domains or contain ambiguity.