These authors have contributed equally to this work.
Version 1
: Received: 21 August 2023 / Approved: 22 August 2023 / Online: 23 August 2023 (08:50:02 CEST)
Version 2
: Received: 24 August 2023 / Approved: 24 August 2023 / Online: 25 August 2023 (08:18:16 CEST)
Version 3
: Received: 28 August 2023 / Approved: 28 August 2023 / Online: 29 August 2023 (08:46:24 CEST)
Version 4
: Received: 7 September 2023 / Approved: 8 September 2023 / Online: 8 September 2023 (07:38:41 CEST)
Version 5
: Received: 14 September 2023 / Approved: 14 September 2023 / Online: 15 September 2023 (04:27:33 CEST)
Version 6
: Received: 11 October 2023 / Approved: 12 October 2023 / Online: 12 October 2023 (03:20:16 CEST)
How to cite:
Bucea Manea Tonis, R.; Beteringhe, A. Making a Grammar Checker with Autocorrect Options Using NLP Tools. Preprints2023, 2023081640. https://doi.org/10.20944/preprints202308.1640.v4
Bucea Manea Tonis, R.; Beteringhe, A. Making a Grammar Checker with Autocorrect Options Using NLP Tools. Preprints 2023, 2023081640. https://doi.org/10.20944/preprints202308.1640.v4
Bucea Manea Tonis, R.; Beteringhe, A. Making a Grammar Checker with Autocorrect Options Using NLP Tools. Preprints2023, 2023081640. https://doi.org/10.20944/preprints202308.1640.v4
APA Style
Bucea Manea Tonis, R., & Beteringhe, A. (2023). Making a Grammar Checker with Autocorrect Options Using NLP Tools. Preprints. https://doi.org/10.20944/preprints202308.1640.v4
Chicago/Turabian Style
Bucea Manea Tonis, R. and Adrian Beteringhe. 2023 "Making a Grammar Checker with Autocorrect Options Using NLP Tools" Preprints. https://doi.org/10.20944/preprints202308.1640.v4
Abstract
Our natural language approach concerns syntactic analysis using a dedicated Javascript library - wink-nlp - and semantic analysis based on Prolog programming language, facilitated by another Javascript library - tau-prolog - that allows defining logical programs, declaring rules and checking for goals inside Javascript language. Firstly, our program splits the original text into sentences, than into tokens and identifies each part of the sentence, dynamically maps entities into Prolog rules, then check the spelling accordingly to the Definite Clause Grammar (DCG) by querying the pre-defined program for initial goals (the sentence itself). Basically, we let the parser infer its own rules from the syntactic point of view, then check the grammar from a semantic perspective against the DCG inside the same work flow or pipeline of steps.The provided article combine the usage of wink-nlp and tau-prolog packages for natural language processing (NLP) and understanding (NLU), demonstrates the need of a supplementary logic layer based on beta reductions and provide a method to convert lambda abstractions into arrow Javascript functions.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Radu BUCEA-MANEA-ȚONIȘ
Commenter's Conflict of Interests: Author