Automatic transcription of 17th century English text in Contemporary English with NooJ: Method and Evaluation

TL;DR

This paper presents a method using NooJ to automatically transcribe 17th century English texts into contemporary English, highlighting linguistic differences and developing tools for lexical and syntactical transcription.

Contribution

It introduces a transcription approach combining lexical and syntactical graphs within NooJ to automate converting archaic forms to modern English.

Findings

Created lexical variant dictionaries and transcription rules

Developed syntactical graphs for archaic form transcription

Proposed a framework for automated transcription of historical texts

Abstract

Since 2006 we have undertaken to describe the differences between 17th century English and contemporary English thanks to NLP software. Studying a corpus spanning the whole century (tales of English travellers in the Ottoman Empire in the 17th century, Mary Astell's essay A Serious Proposal to the Ladies and other literary texts) has enabled us to highlight various lexical, morphological or grammatical singularities. Thanks to the NooJ linguistic platform, we created dictionaries indexing the lexical variants and their transcription in CE. The latter is often the result of the validation of forms recognized dynamically by morphological graphs. We also built syntactical graphs aimed at transcribing certain archaic forms in contemporary English. Our previous research implied a succession of elementary steps alternating textual analysis and result validation. We managed to provide examples of transcriptions, but we have not created a global tool for automatic transcription. Therefore we need to focus on the results we have obtained so far, study the conditions for creating such a tool, and analyze possible difficulties. In this paper, we will be discussing the technical and linguistic aspects we have not yet covered in our previous work. We are using the results of previous research and proposing a transcription method for words or sequences identified as archaic.