This story explains, what is pyspellchecker, and how to use it with SpaCy. The pyspellchecker is an open-source package that allows you to correct spelling.
The pyspellchecker is an open-source package that allows you to correct spelling, as well as see candidate spellings for a misspelled word.
To install the package, you can use pip:
pip install pyspellchecker
First, we import the necessary packages,
Once installed, the pyspellchecker is straightforward to use. Note that even though we use “pyspellchecker” when installing via pip, we just type “spellchecker” in the package import statement.
Fig 1: Import Statements
And to view all the available directories, _dir _method can be used.
Fig 2: Invoking dir method
And the output is all available directories.
[‘_SpellChecker__edit_distance_alt’, ‘__class__’, ‘__contains__’, ‘__delattr__’, ‘__dir__’, ‘__doc__’, ‘__eq__’, ‘__format__’, ‘__ge__’, ‘__getattribute__’, ‘__getitem__’, ‘__gt__’, ‘__hash__’, ‘__init__’, ‘__init_subclass__’, ‘__le__’, ‘__lt__’, ‘__module__’, ‘__ne__’, ‘__new__’, ‘__reduce__’, ‘__reduce_ex__’, ‘__repr__’, ‘__setattr__’, ‘__sizeof__’, ‘__slots__’, ‘__str__’, ‘__subclasshook__’, ‘_case_sensitive’, ‘_check_if_should_check’, ‘_distance’, ‘_tokenizer’, ‘_word_frequency’, ‘candidates’, ‘correction’, ‘distance’, ‘edit_distance_1’, ‘edit_distance_2’, ‘export’, ‘known’, ‘split_words’, ‘unknown’, ‘word_frequency’, ‘word_probability’]
The next piece is to create a SpellChecker object, which we’ll term as “spell”.
Fig 3: Creating an object
And the text we are about to handle is immigrants in Toronto _and the string is stored in the variable docx._
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Pattern is an open-source python library and performs different NLP tasks. It is mostly used for text processing due to various functionalities it provides. Text Processing mainly requires Natural Language Processing( NLP), which is processing the data in a useful way so that the machine can understand the Human Language with the help of an application or product. Using NLP we can derive some information from the textual data such as sentiment, polarity, etc.
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