  High-dimensional distributed semantic spaces have proven useful and effective
for aggregating and processing visual, auditory, and lexical information for
many tasks related to human-generated data. Human language makes use of a large
and varying number of features, lexical and constructional items as well as
contextual and discourse-specific data of various types, which all interact to
represent various aspects of communicative information. Some of these features
are mostly local and useful for the organisation of e.g. argument structure of
a predication; others are persistent over the course of a discourse and
necessary for achieving a reasonable level of understanding of the content.
This paper describes a model for high-dimensional representation for utterance
and text level data including features such as constructions or contextual
data, based on a mathematically principled and behaviourally plausible approach
to representing linguistic information. The implementation of the
representation is a straightforward extension of Random Indexing models
previously used for lexical linguistic items. The paper shows how the
implemented model is able to represent a broad range of linguistic features in
a common integral framework of fixed dimensionality, which is computationally
habitable, and which is suitable as a bridge between symbolic representations
such as dependency analysis and continuous representations used e.g. in
classifiers or further machine-learning approaches. This is achieved with
operations on vectors that constitute a powerful computational algebra,
accompanied with an associative memory for the vectors. The paper provides a
technical overview of the framework and a worked through implemented example of
how it can be applied to various types of linguistic features.
