Development of neural network based rules for confusion set disambiguation in LanguageTool

Abstract

Confusion set disambiguation is a typical task for grammar checkers like LanguageTool. In this paper we present a neural network based approach which has low memory requirements, high precision with decent recall, and can easily be integrated into LanguageTool. Furthermore, adding support for new confusion pairs does not need any knowledge of the target language. We examine different sampling techniques and neural network architectures and compare our approaches with an existing memory-based algorithm.