Demonstrating conceptual dynamics in an evolving text collection

Abstract

Based on real world user demands, we demonstrate how animated visualisation of evolving text corpora displays the underlying dynamics of semantic content. To interpret the results, one needs a dynamic theory of word meaning. We suggest that conceptual dynamics as the interaction between kinds of intellectual, emotional etc. content, and language, is key for such a theory. We demonstrate our methodology by two-way seriation which is a popular technique to analyse groups of similar instances and their features, as well as the connections between the groups themselves. The two-way seriated data may be visualised as a two-dimensional heat map or as a three-dimensional landscape where colour codes or height correspond to the values in the matrix. In this paper we focus on two-way seriation of sparse data in the Reuters-21568 test collection. To achieve a meaningful visualisation thereof we introduce a compactly supported convolution kernel similar to filter kernels used in image reconstruction and geostatistics. This filter populates the high-dimensional sparse space with values that interpolate nearby elements, and provides insight into the clustering structure. We also extend two-way seriation to deal with online updates of both the row and column spaces, and, combined with the convolution kernel, demonstrate a three-dimensional visualisation of dynamics.

A Vector Field Approach to Lexical Semantics

“…More importantly, using the concept of kinds [29], one can look into the dynamic "origins" of distributions of related content. To this end, one must add interpolation between located items of meaning such as by Gaussian blurring [30] -its role is to approximate a previous state of potentiality from observation as actuality. The result will show lexical gaps as metaphoric fault lines in plate tectonics, with new semantic content about to protrude.…”