contextualSpellCheck 0.3.3

Contextual spell correction using BERT (bidirectional representations)

spellCheck

Contextual word checker for better suggestions

Types of spelling mistakes

It is essential to understand that identifying whether a candidate is a spelling error is a big task. You can see the below quote from a research paper:

Spelling errors are broadly classified as non- word errors (NWE) and real word errors (RWE). If the misspelt string is a valid word in the language, then it is called an RWE, else it is an NWE.

-- Monojit Choudhury et. al. (2007)

This package currently focuses on Out of Vocabulary (OOV) word or non-word error (NWE) correction using BERT model. The idea of using BERT was to use the context when correcting OOV. In the coming days, I would like to focus on RWE and optimising the package by implementing it in cython.

Install

The package can be installed using pip. You would require python 3.6+

pip install contextualSpellCheck

Also, please install the dependencies from requirements.txt

Usage

Note: For other language examples check examples folder.

How to load the package in spacy pipeline

>>> import contextualSpellCheck
>>> import spacy
>>> 
>>> ## We require NER to identify if it is PERSON
>>> ## also require parser because we use Token.sent for context
>>> nlp = spacy.load("en_core_web_sm") 
>>> 
>>> contextualSpellCheck.add_to_pipe(nlp)
<spacy.lang.en.English object at 0x12839a2d0>
>>> nlp.pipe_names
['tagger', 'parser', 'ner', 'contextual spellchecker']
>>> 
>>> doc = nlp('Income was $9.4 milion compared to the prior year of $2.7 milion.')
>>> doc._.outcome_spellCheck
'Income was $9.4 million compared to the prior year of $2.7 million.'

Or you can add to spaCy pipeline manually!

>>> import spacy
>>> import contextualSpellCheck
>>> 
>>> nlp = spacy.load('en')
>>> checker = contextualSpellCheck.contextualSpellCheck.ContextualSpellCheck()
>>> nlp.add_pipe(checker)
>>> 
>>> doc = nlp("Income was $9.4 milion compared to the prior year of $2.7 milion.")
>>> print(doc._.performed_spellCheck)
True
>>> print(doc._.outcome_spellCheck)
Income was $9.4 million compared to the prior year of $2.7 million.

After adding contextual spell checker in the pipeline, you use the pipeline normally. The spell check suggestions and other data can be accessed using extensions.

Using the pipeline

>>> doc = nlp(u'Income was $9.4 milion compared to the prior year of $2.7 milion.')
>>> 
>>> # Doc Extention
>>> print(doc._.contextual_spellCheck)
True
>>> print(doc._.performed_spellCheck)
True
>>> print(doc._.suggestions_spellCheck)
{milion: 'million', milion: 'million'}
>>> print(doc._.outcome_spellCheck)
Income was $9.4 million compared to the prior year of $2.7 million.
>>> print(doc._.score_spellCheck)
{milion: [('million', 0.59422), ('billion', 0.24349), (',', 0.08809), ('trillion', 0.01835), ('Million', 0.00826), ('%', 0.00672), ('##M', 0.00591), ('annually', 0.0038), ('##B', 0.00205), ('USD', 0.00113)], milion: [('billion', 0.65934), ('million', 0.26185), ('trillion', 0.05391), ('##M', 0.0051), ('Million', 0.00425), ('##B', 0.00268), ('USD', 0.00153), ('##b', 0.00077), ('millions', 0.00059), ('%', 0.00041)]}
>>> 
>>> # Token Extention
>>> print(doc[4]._.get_require_spellCheck)
True
>>> print(doc[4]._.get_suggestion_spellCheck)
'million'
>>> print(doc[4]._.score_spellCheck)
[('million', 0.59422), ('billion', 0.24349), (',', 0.08809), ('trillion', 0.01835), ('Million', 0.00826), ('%', 0.00672), ('##M', 0.00591), ('annually', 0.0038), ('##B', 0.00205), ('USD', 0.00113)]
>>> 
>>> # Span Extention
>>> print(doc[2:6]._.get_has_spellCheck)
True
>>> print(doc[2:6]._.score_spellCheck)
{$: [], 9.4: [], milion: [('million', 0.59422), ('billion', 0.24349), (',', 0.08809), ('trillion', 0.01835), ('Million', 0.00826), ('%', 0.00672), ('##M', 0.00591), ('annually', 0.0038), ('##B', 0.00205), ('USD', 0.00113)], compared: []}

Extensions

To make the usage simpler spacy provides custom extensions which a library can use. This makes it easier for the user to get the desired data. contextualSpellCheck provides extensions on the doc, span and token level. Below tables summaries the extensions.

spaCy.Doc level extensions

Extension	Type	Description	Default
doc._.contextual_spellCheck	Boolean	To check whether contextualSpellCheck is added as extension	True
doc._.performed_spellCheck	Boolean	To check whether contextualSpellCheck identified any misspells and performed correction	False
doc._.suggestions_spellCheck	{Spacy.Token:str}	if corrections are performed, it returns the mapping of misspell token (spaCy.Token) with suggested word(str)	{}
doc._.outcome_spellCheck	str	corrected sentence(str) as output	""
doc._.score_spellCheck	{Spacy.Token:List(str,float)}	if corrections are identified, it returns the mapping of misspell token (spaCy.Token) with suggested words(str) and probability of that correction	None

spaCy.Span level extensions

Extension	Type	Description	Default
span._.get_has_spellCheck	Boolean	To check whether contextualSpellCheck identified any misspells and performed correction in this span	False
span._.score_spellCheck	{Spacy.Token:List(str,float)}	if corrections are identified, it returns the mapping of misspell token (spaCy.Token) with suggested words(str) and probability of that correction for tokens in this span	{spaCy.Token: []}

spaCy.Token level extensions

Extension	Type	Description	Default
token._.get_require_spellCheck	Boolean	To check whether contextualSpellCheck identified any misspells and performed correction on this token	False
token._.get_suggestion_spellCheck	str	if corrections are performed, it returns the suggested word(str)	""
token._.score_spellCheck	[(str,float)]	if corrections are identified, it returns suggested words(str) and probability(float) of that correction	[]

API

At present, there is a simple GET API to get you started. You can run the app in your local and play with it.

Query: You can use the endpoint http://127.0.0.1:5000/?query=YOUR-QUERY Note: Your browser can handle the text encoding

http://localhost:5000/?query=Income%20was%20$9.4%20milion%20compared%20to%20the%20prior%20year%20of%20$2.7%20milion.

Response:

{
    "success": true,
    "input": "Income was $9.4 milion compared to the prior year of $2.7 milion.",
    "corrected": "Income was $9.4 milion compared to the prior year of $2.7 milion.",
    "suggestion_score": {
        "milion": [
            [
                "million",
                0.59422
            ],
            [
                "billion",
                0.24349
            ],
            ...
        ],
        "milion:1": [
            [
                "billion",
                0.65934
            ],
            [
                "million",
                0.26185
            ],
            ...
        ]
    }
}

Task List

• Add support for Real Word Error (RWE) (Big Task)
• edit distance code optimisation
• add multi mask out capability
• better candidate generation (maybe by fine tuning the model?)
• add metric by testing on datasets
• Improve documentation
• Add examples for other langauges
• use piece wise tokeniser when identifying the misspell
• Improve logging in code
• Update the logic of misspell identification (OOV) (#30)

Completed Task

• specify maximum edit distance for candidateRanking
• allow user to specify bert model
• Include transformers deTokenizer to get better suggestions

Support and contribution

If you like the project, please ⭑ the project and show your support! Also, if you feel, the current behaviour is not as expected, please feel free to raise an issue. If you can help with any of the above tasks, please open a PR with necessary changes to documentation and tests.

Reference

Below are some of the projects/work I referred to while developing this package

1. Spacy Documentation and custom attributes
2. HuggingFace's Transformers
3. Norvig's Blog
4. Bert Paper: https://arxiv.org/abs/1810.04805
5. Denoising words: https://arxiv.org/pdf/1910.14080.pdf
6. CONTEXT BASED SPELLING CORRECTION (1990)
7. How Difficult is it to Develop a Perfect Spell-checker? A Cross-linguistic Analysis through Complex Network Approach
8. HuggingFace's neuralcoref for package design and some of the functions are inspired from them (like add_to_pipe which is an amazing idea!)