Machine Learning-Based Spellchecker

Weaknesses of traditional spellcheckers

Both the Norvig and SymSpell spellcheckers provide tremendous speed and very good accuracy. However, they are primarily limited by the dictionary they are using, and human-coded heuristics and rules are used to find similar words. The models are inherently not designed to learn and adapt. In the last few years, several new machine learning-based methods have been developed to face these challenges. Here are some expansions to our spellchecker that machine learning can provide:

Advantages of ML-based spellcheckers

1. Contextual understanding: Machine learning-based spell correctors have the ability to capture and utilize contextual information. They can analyze the surrounding words and sentence structure to better infer the intended word or phrase. Traditional methods, such as rule-based or dictionary-based approaches, typically lack this contextual understanding and rely solely on predefined rules or word lists. Most ML methods are n-gram-based models instead of our noisy channel models, a much-needed improvement to achieve high accuracy, as even the best non-ML n-gram models lack complex relationships and often rely on simple heuristics.

2. Adaptability and extensibility: Machine learning models can be trained to learn patterns and relationships between words. They adapt to different domains, languages, and writing styles by generalizing from the training data. Traditional methods, on the other hand, often require manual rule creation or different dictionaries for different languages, making them less adaptable.

3. Handling out-of-vocabulary words: Machine learning-based spell correctors can handle out-of-vocabulary words, which are words not present in the training dictionary. By learning statistical patterns from the data, these models then suggest corrections for previously unseen or rare words. Traditional methods may struggle with out-of-vocabulary words as they rely heavily on the predefined dictionary.  ML-based methods are able to better conjugate and guess the correct tense of misspelled words. This allows them to work hand in hand with grammar models.

4. Non-linear relationships: Machine learning models can capture non-linear relationships between characters or words. They learn complex patterns, including phonetic similarities, letter transpositions, and other linguistic nuances. Traditional methods often use simpler rules and heuristics that may not effectively capture these intricate relationships. For example, an ML model could intrinsically learn which errors are more common than others when trying to spell certain words based on training data consisting of mappings to common errors or synthetic error creation.

5. Continuous improvement: Machine learning models can be continually updated and improved. As more data becomes available, the models can be retrained to incorporate new information and refine their spell correction capabilities. Traditional methods, once implemented, often require manual updates and maintenance. This would often include updating to a more modern corpus.

6. Data-driven approach: Machine learning-based spell correctors make use of extensive training data, which enables them to leverage statistical information and make more accurate predictions. They can identify common misspellings, detect contextual errors, and suggest corrections based on the most likely alternatives. Traditional methods rely on predefined rules or dictionaries, which may not cover all possible variations and edge cases.

In the rest of this section, we will deep dive into one such machine learning-based method and transformer-based language models for spell check, and we can discover how some of the limitations of our previous spellcheckers are addressed.