Mastering Natural Language Processing
with Python
Tokenization - answer______________ may be defined as the process of splitting the
text into smaller parts called tokens, and is considered a crucial step in NLP.
sent tokenize(text) - answer Tokenization of text into sentences with nltk
word tokenize(text) - answer Tokenization of sentences into words with nltk
Normalization - answer In order to carry out processing on natural language text, we
need to perform
______________ that mainly involves eliminating punctuation, converting the entire text
into lowercase or uppercase, converting numbers into words, expanding abbreviations,
canonicalization of text, and so on.
normalization - answerThe task of converting text into uppercase or
lowercase falls under the category of ___________.
Stop words - answer_______________ are words that need to be filtered out during the
task of information retrieval or other natural language tasks, as these words do not
contribute much to the overall meaning of the sentence.
Zipf's law - answer________ states that the frequency of a token in a text is directly
proportional to its rank or position in the sorted list. This law describes how tokens are
distributed in languages: some tokens occur very frequently, some occur with
intermediate frequency, and some tokens rarely occur.
Levenshtein - answerEdit distance or the _________ edit distance between two strings
is used to
compute the number of characters that can be inserted, substituted, or deleted in
order to make two strings equal.
Jaccard - answer______'s coefficient, or Tanimoto coefficient, may be defined as a
measure of the
overlap of two sets, X and Y.
Smith Waterman - answerThe __________ distance is similar to edit distance. This
similarity metric
was developed in order to detect the optical alignments between related protein
sequences and DNA.
with Python
Tokenization - answer______________ may be defined as the process of splitting the
text into smaller parts called tokens, and is considered a crucial step in NLP.
sent tokenize(text) - answer Tokenization of text into sentences with nltk
word tokenize(text) - answer Tokenization of sentences into words with nltk
Normalization - answer In order to carry out processing on natural language text, we
need to perform
______________ that mainly involves eliminating punctuation, converting the entire text
into lowercase or uppercase, converting numbers into words, expanding abbreviations,
canonicalization of text, and so on.
normalization - answerThe task of converting text into uppercase or
lowercase falls under the category of ___________.
Stop words - answer_______________ are words that need to be filtered out during the
task of information retrieval or other natural language tasks, as these words do not
contribute much to the overall meaning of the sentence.
Zipf's law - answer________ states that the frequency of a token in a text is directly
proportional to its rank or position in the sorted list. This law describes how tokens are
distributed in languages: some tokens occur very frequently, some occur with
intermediate frequency, and some tokens rarely occur.
Levenshtein - answerEdit distance or the _________ edit distance between two strings
is used to
compute the number of characters that can be inserted, substituted, or deleted in
order to make two strings equal.
Jaccard - answer______'s coefficient, or Tanimoto coefficient, may be defined as a
measure of the
overlap of two sets, X and Y.
Smith Waterman - answerThe __________ distance is similar to edit distance. This
similarity metric
was developed in order to detect the optical alignments between related protein
sequences and DNA.
