High-dimensional distributed semantic spaces for utterances

TL;DR

This paper introduces a high-dimensional, mathematically principled model for representing diverse linguistic features of utterances and texts, bridging symbolic and continuous representations for improved language processing.

Contribution

It extends Random Indexing to create a unified, fixed-dimensional vector space for various linguistic features, enabling efficient integration of symbolic and machine learning methods.

Findings

Successfully represents a broad range of linguistic features in a common vector space

Demonstrates the model's applicability to different linguistic data types

Provides a computationally feasible approach for linguistic feature integration

Abstract

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.