Text Preprocessing
Cleaning and preparation are crucial for many tasks, and NLP is no exception. Text
preprocessing is usually the first step you'll take when faced with an NLP task. - answer
Without preprocessing, your computer interprets "the", "The", and "<p>The" as entirely
different words.
There is a LOT you can do here, depending on the formatting you need. Lucky for you,
Regex and NLTK will do most of it for you! Common tasks include: - answer Noise
removal — stripping text of formatting (e.g., HTML tags)
Noise removal - answer stripping text of formatting (e.g., HTML tags)
Tokenization - answer breaking text into individual words.
Normalization - answer cleaning text data in any other way:
Stemming - answer Stemming is a blunt axe to chop off word prefixes and suffixes.
"booing" and "booed" become "boo", but "sing" may become "s" and "sung" would
remain "sung."
Lemmatization - answer Lemmatization is a scalpel to bring words down to their root
forms. For example, NLTK's savvy lemmatize knows "am" and "are" are related to "be."
Other common tasks include lowercasing, punctuation removal, stopwords removal,
spelling correction, etc. - answer Other common tasks include lowercasing, punctuation
removal, stop words removal, spelling correction, etc.
Cleaning and preparation are crucial for many tasks, and NLP is no exception. Text
preprocessing is usually the first step you'll take when faced with an NLP task. - answer
Without preprocessing, your computer interprets "the", "The", and "<p>The" as entirely
different words.
There is a LOT you can do here, depending on the formatting you need. Lucky for you,
Regex and NLTK will do most of it for you! Common tasks include: - answer Noise
removal — stripping text of formatting (e.g., HTML tags)
Noise removal - answer stripping text of formatting (e.g., HTML tags)
Tokenization - answer breaking text into individual words.
Normalization - answer cleaning text data in any other way:
Stemming - answer Stemming is a blunt axe to chop off word prefixes and suffixes.
"booing" and "booed" become "boo", but "sing" may become "s" and "sung" would
remain "sung."
Lemmatization - answer Lemmatization is a scalpel to bring words down to their root
forms. For example, NLTK's savvy lemmatize knows "am" and "are" are related to "be."
Other common tasks include lowercasing, punctuation removal, stopwords removal,
spelling correction, etc. - answer Other common tasks include lowercasing, punctuation
removal, stop words removal, spelling correction, etc.
