manual-spellchecker 1.0

A manual spell checker built on pyenchant that allows you to swiftly correct misspelled words.

Manual Spell Checker

A manual spell checker built on pyenchant that allows you to swiftly correct misspelled words.

Why does this exist?

While I was working on a text based multi-class classification competition, I noticed that the data contained a lot of misspelled words, errors which automated spell check packages out there couldn't fix. This was because the data had been compiled based on a survey of people who weren't native English speakers. As the there weren't many samples in the dataset (~1000), I decided to write some code for automated detection of spelling errors which I could then fix manually, and thus, this package was born.

How to install?

pip install manual_spellchecker

How to use it?

Parameters

• dataframe - Takes a pandas dataframe as input
• column_names - Pass the column names upon which you want to perform spelling correction
• tokenizer=None - Pass your favourite tokenizer like nltk or spacy, etc. (Default: splits on space)
• num_n_words_dis=5 - This decides how many neighbouring words to display on either side of the error
• save_path=None - If a save path is provided, the final corrected dataframe is saved as a csv. (Default: the dataframe is not saved externally)

Functions

• spell_check - Prints the total number of suspected errors
• get_all_errors - Returns a list of all the suspected errors
• correct_words - Starts the process of manual correction

Important Note: Type -999 into the input box to stop the error correction and save the current progress (is save_path is provided) P.S.: As the package is built on pyenchant, it also provides suggestions while performing corrections

How to import?

from manual_spellchecker import spell_checker

Quick analysis of the total number of errors

df = pd.read_csv("Train.csv")
# Initialize the model
ob = spell_checker(df, "text")
# Quick analysis
ob.spell_check()

Multiple columns can be passed for spelling correction

df = pd.read_csv("Train.csv")
# Initialize the model
ob = spell_checker(df, ["text", "label"])
# Quick analysis
ob.spell_check()

Tokenizers affect the type/number of error(s)

from nltk.tokenize import word_tokenize
df = pd.read_csv("Train.csv")
# Initialize the model
ob = spell_checker(df, "text", tokenizer=word_tokenize)
# Quick analysis
ob.spell_check()

Get a list of all the errors

df = pd.read_csv("Train.csv")
# Initialize the model
ob = spell_checker(df, "text")
# Get all the errors as a list
ob.get_all_errors()

Make corrections

df = pd.read_csv("Train.csv")
# Initialize the model
ob = spell_checker(df, "text")
# Perform correction
ob.correct_words()

To save

df = pd.read_csv("Train.csv")
# Initialize the model
ob = spell_checker(df, "text", save_path="correct_train_data.csv")

Future Ideas

• Will be adding automated, contextual error corrections

Feature Request

Drop me an email at [e-mail] if you want any particular feature