Natural Language Processing: Python and NLTK

Your turn

Here are the answers to the open-ended questions:

• Try to connect any of the data base using pyodbc.

  https://code.google.com/p/pyodbc/wiki/GettingStarted

• Can you build a regex tokenizer that will only select words that are either small, capitals, numbers or money symbols?

  [\w+] selects all the words and numbers [a-z A-Z 0-9] and [\$] will match money symbol.

• What's the difference between Stemming and lemmatization?

  Stemming is more of a rule-based approach to get the root of the word's grammatical forms, while lemmatization also considers context and the POS of the given word, then applies rules specific to grammatical variants. Stemmers are easier to implement and the processing time is faster than lemmatizer.

• Can you come up with a Porter ...