A Category Theory Approach to Interoperability

TL;DR

This paper introduces a novel approach using Category Theory to model and improve interoperability among linguistic tools and NLP pipelines, enabling better integration and format conversion.

Contribution

It applies Category Theory concepts to formalize NLP tool pipelines and demonstrates this approach with real-world examples, enhancing interoperability.

Findings

Modeling NLP pipelines with Category Theory is effective.

The approach facilitates format conversions between tools.

Successful application to real-life linguistic tool examples.

Abstract

In this article, we propose a Category Theory approach to (syntactic) interoperability between linguistic tools. The resulting category consists of textual documents, including any linguistic annotations, NLP tools that analyze texts and add additional linguistic information, and format converters. Format converters are necessary to make the tools both able to read and to produce different output formats, which is the key to interoperability. The idea behind this document is the parallelism between the concepts of composition and associativity in Category Theory with the NLP pipelines. We show how pipelines of linguistic tools can be modeled into the conceptual framework of Category Theory and we successfully apply this method to two real-life examples.