Generating Semantic Graph Corpora with Graph Expansion Grammar

TL;DR

Lovelace is a tool that generates semantic graph corpora using graph expansion grammar, enabling customizable and well-formed graph creation for synthetic data augmentation and educational purposes.

Contribution

The paper introduces Lovelace, a novel system that leverages graph expansion grammar for controlled generation of semantic graph corpora.

Findings

Enables creation of customizable semantic graph corpora.

Supports augmentation of existing datasets with synthetic graphs.

Serves as an educational tool for formal language theory.

Abstract

We introduce Lovelace, a tool for creating corpora of semantic graphs. The system uses graph expansion grammar as a representational language, thus allowing users to craft a grammar that describes a corpus with desired properties. When given such grammar as input, the system generates a set of output graphs that are well-formed according to the grammar, i.e., a graph bank. The generation process can be controlled via a number of configurable parameters that allow the user to, for example, specify a range of desired output graph sizes. Central use cases are the creation of synthetic data to augment existing corpora, and as a pedagogical tool for teaching formal language theory.