This repository implements a brute-force spellchecker utilizing the Damerau-Levenshtein edit distance.

Overview

About spellchecker.py

Implementing a highly-accurate, brute-force, and dynamically programmed spellchecking program that utilizes the Damerau-Levenshtein string metric for measuring edit distance between two sequences of characters.

How to Write Your Own Test Cases

In the lib folder, you will see two different text files called 'candidate_words.txt' and 'incorrect_words.txt':

  • The candidate_words.txt text file can contain an unlimited amount of CORRECTLY spelled words, with each word written on a new line.
  • The incorrect_words.txt text file can contain an unlimited amount of INCORRECTLY spelled words, with each word written on a new line. However, each incorrectly spelled word in this list MUST have its correctly spelled counterpart contained somewhere in the 'candidate_words.txt' text file. It doesn't matter where, since the 'candidate_words.txt' file will be randomly shuffled anyway.

In the test folder, you will see a text file called target_words.txt:

  • The 'target_words.txt' file will contain the CORRECT spelling of each word contained in the 'incorrect_words.txt' text file, with each being on a new line in the same exact order that you inserted their incorrectly spelled counterparts in the 'incorrect_words.txt' text file. It is important that both the incorrectly and correctly spelled words are in the same order to be able to calculate the accuracy of the spell checker.

To view an example on how to create your own test cases, take a look at the files provided in either folder.

How to Run the Program

Enter the folder's directory using your terminal. Then, simply run python3 spellchecker.py

  • The only thing you will need to modify are the files in the lib and test folders if you want to try the program with your own test cases. The program does not need to be touched, unless you'd like to modify the global variable 'THRESHOLD', which is used as the threshold to find an incorrectly spelled word's closest approximation.
  • The incorrectly spelled words in 'incorrect_words.txt' will be run through the program to find its closest lexical match from the candidate_words.txt text file using the Damerau-Levenshtein algorithm.
  • The spellchecked words will then be, in order, cross checked against its intended counterparts in target_words.txt to calculate the overall accuracy of the spellchecking algorithm.

The results of the program will then be printed to your terminal.

Dependencies

Ensure that you have difflib installed for python3.

Final Words

Feel free to use or modify this program for your intended purposes!

Owner
Raihan Ahmed
Pursuing a degree in CS with concentrations in Computer Science, Computer Networks and Security, and Information Technology. Minoring in Linguistics.
Raihan Ahmed
Multilingual finetuning of Machine Translation model on low-resource languages. Project for Deep Natural Language Processing course.

Low-resource-Machine-Translation This repository contains the code for the project relative to the course Deep Natural Language Processing. The goal o

Andrea Cavallo 3 Jun 22, 2022
This repository contains all the source code that is needed for the project : An Efficient Pipeline For Bloom’s Taxonomy Using Natural Language Processing and Deep Learning

Pipeline For NLP with Bloom's Taxonomy Using Improved Question Classification and Question Generation using Deep Learning This repository contains all

Rohan Mathur 9 Jul 17, 2021
Model parallel transformers in JAX and Haiku

Table of contents Mesh Transformer JAX Updates Pretrained Models GPT-J-6B Links Acknowledgments License Model Details Zero-Shot Evaluations Architectu

Ben Wang 4.9k Jan 04, 2023
Code for text augmentation method leveraging large-scale language models

HyperMix Code for our paper GPT3Mix and conducting classification experiments using GPT-3 prompt-based data augmentation. Getting Started Installing P

NAVER AI 47 Dec 20, 2022
自然言語で書かれた時間情報表現を抽出/規格化するルールベースの解析器

ja-timex 自然言語で書かれた時間情報表現を抽出/規格化するルールベースの解析器 概要 ja-timex は、現代日本語で書かれた自然文に含まれる時間情報表現を抽出しTIMEX3と呼ばれるアノテーション仕様に変換することで、プログラムが利用できるような形に規格化するルールベースの解析器です。

Yuki Okuda 116 Nov 09, 2022
Python port of Google's libphonenumber

phonenumbers Python Library This is a Python port of Google's libphonenumber library It supports Python 2.5-2.7 and Python 3.x (in the same codebase,

David Drysdale 3.1k Dec 29, 2022
☀️ Measuring the accuracy of BBC weather forecasts in Honolulu, USA

Accuracy of BBC Weather forecasts for Honolulu This repository records the forecasts made by BBC Weather for the city of Honolulu, USA. Essentially, t

Max Halford 12 Oct 15, 2022
Test finetuning of XLSR (multilingual wav2vec 2.0) for other speech classification tasks

wav2vec_finetune Test finetuning of XLSR (multilingual wav2vec 2.0) for other speech classification tasks Initial test: gender recognition on this dat

8 Aug 11, 2022
CLIPfa: Connecting Farsi Text and Images

CLIPfa: Connecting Farsi Text and Images OpenAI released the paper Learning Transferable Visual Models From Natural Language Supervision in which they

Sajjad Ayoubi 66 Dec 14, 2022
[NeurIPS 2021] Code for Learning Signal-Agnostic Manifolds of Neural Fields

Learning Signal-Agnostic Manifolds of Neural Fields This is the uncleaned code for the paper Learning Signal-Agnostic Manifolds of Neural Fields. The

60 Dec 12, 2022
Material for GW4SHM workshop, 16/03/2022.

GW4SHM Workshop Wednesday, 16th March 2022 (13:00 – 15:15 GMT): Presented by: Dr. Rhodri Nelson, Imperial College London Project website: https://www.

Devito Codes 1 Mar 16, 2022
Py65 65816 - Add support for the 65C816 to py65

Add support for the 65C816 to py65 Py65 (https://github.com/mnaberez/py65) is a

4 Jan 04, 2023
Simple NLP based project without any use of AI

Simple NLP based project without any use of AI

Shripad Rao 1 Apr 26, 2022
The guide to tackle with the Text Summarization

The guide to tackle with the Text Summarization

Takahiro Kubo 1.2k Dec 30, 2022
Code for our paper "Mask-Align: Self-Supervised Neural Word Alignment" in ACL 2021

Mask-Align: Self-Supervised Neural Word Alignment This is the implementation of our work Mask-Align: Self-Supervised Neural Word Alignment. @inproceed

THUNLP-MT 46 Dec 15, 2022
Module for automatic summarization of text documents and HTML pages.

Automatic text summarizer Simple library and command line utility for extracting summary from HTML pages or plain texts. The package also contains sim

Mišo Belica 3k Jan 08, 2023
The following links explain a bit the idea of semantic search and how search mechanisms work by doing retrieve and rerank

Main Idea The following links explain a bit the idea of semantic search and how search mechanisms work by doing retrieve and rerank Semantic Search Re

Sergio Arnaud Gomez 2 Jan 28, 2022
Main repository for the chatbot Bobotinho.

Bobotinho Bot Main repository for the chatbot Bobotinho. ℹ️ Introduction Twitch chatbot with entertainment commands. ‎ 💻 Technologies Concurrent code

Bobotinho 14 Nov 29, 2022
This repository contains the code, data, and models of the paper titled "CrossSum: Beyond English-Centric Cross-Lingual Abstractive Text Summarization for 1500+ Language Pairs".

CrossSum This repository contains the code, data, and models of the paper titled "CrossSum: Beyond English-Centric Cross-Lingual Abstractive Text Summ

BUET CSE NLP Group 29 Nov 19, 2022
Torchrecipes provides a set of reproduci-able, re-usable, ready-to-run RECIPES for training different types of models, across multiple domains, on PyTorch Lightning.

Recipes are a standard, well supported set of blueprints for machine learning engineers to rapidly train models using the latest research techniques without significant engineering overhead.Specifica

Meta Research 193 Dec 28, 2022