Python computer science technology .pptx

Python computer science technology .pptx

• 1.
  Python + NLTK NaturalLanguage Processing
• 2.
  Brief History ofPython • Invented in the Netherlands, early 90s by Guido van Rossum • Named after Monty Python • Open sourced from the beginning • Used by Google from the beginning • Increasingly popular • https://www.python.org/downloads/
• 3.
  Naming Rules • Namesare case sensitive and cannot start with a number. They can contain letters, numbers, and underscores. bob Bob _bob _2_bob_ bob_2 BoB • There are some reserved words: and, assert, break, class, continue, def, del, elif, else, except, exec, finally, for, from, global, if, import, in, is, lambda, not, or, pass, print, raise, return, try, while
• 4.
  Assignment • You canassign to multiple names at the same time >>> x, y = 2, 3 >>> x 2 >>> y 3 This makes it easy to swap values >>> x, y = y, x • Assignments can be chained >>> a = b = x = 2
• 5.
  if elseelse elif
• 6.
  A Code Sample(in IDLE) x = 10 - 5 # A comment. y = “Hello” # Another one. z = 3.45 if z == 3.45 or y == “Hello”: x = x + 1 y = y + “ World” # String concat. print x print y
• 7.
  Array • array =[2, 54, 5, 7, 8, 9] • list1 = list() #empty list
• 8.
  NLTK • NLTK (NaturalLanguage Toolkit) is the go-to API for NLP (Natural Language Processing) with Python pip install nltk
• 9.
  Tokenization • Tokenization isa way of separating a piece of text into smaller units called tokens. • sentence: “Never give up”. • 3 tokens – Never-give-up.
• 10.
  • from nltkimport word_tokenize, sent_tokenize • sent = "I will walk 500 miles and I would walk 500 more, just to be the man who walks a thousand miles to fall down at your door!“ • print(word_tokenize(sent)) • print(sent_tokenize(sent))
• 11.
  Stop words • Stopwords are the words which are very common in text documents • Example • as a, an, the, you, your, etc. • Print all stopwords in English
• 12.
  Stop Word Removal •from nltk.corpus import stopwords • from nltk.tokenize import word_tokenize • example_sent = """early symptoms of the coronavirus""" • stop_words = set(stopwords.words('english')) • word_tokens = word_tokenize(example_sent) • filtered_sentence = [w for w in word_tokens if not w in stop_words]
• 13.
  Stop Word Removal •filtered_sentence = [] • for w in word_tokens: • if w not in stop_words: • filtered_sentence.append(w) • print(word_tokens) • print(filtered_sentence)
• 14.
  Stemming • Stemming isthe process of producing morphological variants of a root/base word. • Stemming is used in information retrieval systems like search engines. • It is used to determine domain vocabularies in domain analysis. • Some more example of stemming for root word "like" include: -> "likes" -> "liked" -> "likely" Some more example of stemming for root word "like" include: -> "likes" -> "liked" -> "likely" -> "liking"