Introduction to Contextual Spell Check
ContextualSpellCheck is an innovative project designed to enhance spelling correction capabilities by providing better suggestions based on context. The project leverages the power of the BERT model, a leading deep learning technique, to offer corrections that consider the surrounding text, which is crucial for identifying and rectifying out-of-vocabulary (OOV) or non-word errors. The aim is to improve spelling suggestions, especially in scenarios where traditional spell checkers might struggle.
Understanding Spelling Mistakes
Spelling mistakes can be classified into two broad categories: non-word errors and real-word errors. Non-word errors occur when a string is not a valid word in the language, while real-word errors happen when a misspelling still forms a valid word but is incorrect in the given context. ContextualSpellCheck currently focuses on correcting non-word errors using contextual analysis provided by BERT.
Installation
Getting started with ContextualSpellCheck is simple. The package is distributed via PyPI and can be installed using pip with Python version 3.6 or higher:
pip install contextualSpellCheck
Usage
The package is designed to integrate seamlessly with the spaCy NLP pipeline. Here’s a basic example:
import contextualSpellCheck
import spacy
nlp = spacy.load("en_core_web_sm")
contextualSpellCheck.add_to_pipe(nlp)
doc = nlp('Income was $9.4 milion compared to the prior year of $2.7 milion.')
print(doc._.outcome_spellCheck) # Outputs: 'Income was $9.4 million compared to the prior year of $2.7 million.'
Features and Extensions
The package allows users to access spell check suggestions and additional data through spaCy custom extensions at various levels, including document, span, and token. This makes it easy for users to retrieve and consume the results for further processing within their applications.
Document-Level Extensions
- contextual_spellCheck: Confirms if the extension is active.
- performed_spellCheck: Indicates if any corrections were made.
- suggestions_spellCheck: Provides a mapping of misspelled tokens to suggested words.
- outcome_spellCheck: Delivers the corrected text.
- score_spellCheck: Offers probability scores for suggested corrections.
Span and Token-Level Extensions
Similar extensions are available at the span and token levels, allowing for detailed and specific correction analysis.
API Availability
An API is available for simple GET requests, which allows users to test and use the spell check feature via a web interface. It is operational on a local server setup, providing ease of experimentation and integration into existing systems.
Future Development and Contribution
The project is open-source and actively seeking contributions. Current development goals include optimizing performance, expanding the model to handle real-word errors, improving metric evaluation, and enhancing documentation. Users and developers are encouraged to contribute or report issues to help improve the project further.
Conclusion
ContextualSpellCheck is a powerful tool for anyone dealing with text data, offering an advanced approach to spell-checking that considers the context within which a word appears. By integrating state-of-the-art machine learning models, it provides more accurate corrections and opens up new possibilities for text processing applications.