Please kindly provide the code in python: Submit a single file Python program that does the following:

$1.$ Identify three different semantic frames in FrameNet that you would like to detect in text $($your

choice$).$

$2.$ Using Wikipedia for your corpus candidates, choose three articles for each of the three frames to

detect instances of the respective frames in text $($up to nine articles total, some articles may be used

for more than one case$)$

$3.$ For each of your three chosen semantic frames, develop linguistic criteria for identifying an instance

in text. Hint: Look for $($a$)$ a key Lexical Unit that evokes the frame, for example $$buy$$ or $$purchase$$

for the Commercial$\_$transaction frame, and $($b$)$ the other Frame Elements $($FEs$)$ that meet the

description from the frame$$part of speech, WordNet synset category, particular type of named entity,

etc. $4.$ Implement the criteria from #$3$ in Python$/$NLTK to use on your chosen Wikipedia articles. You may

use any of the techniques we have covered so far to detect words $($i$.$e$.$ lexemes, lexical units$)$ that

evoke $($i$.$e$.$ signal, indicate$)$ a particular semantic frame.

$4\mathrm{.}1.$ Tokenization

$4\mathrm{.}2.$ Normalization

$4\mathrm{.}3.$ Regular expressions

$4\mathrm{.}4.$ POS tagging

$4\mathrm{.}5.$ WordNet synsets

$4\mathrm{.}6.$ Chunking

$4\mathrm{.}7.$ Named Entity recognition

$4\mathrm{.}8.$ Gazetteer

You may display your output in any format you choose. However, please display a header to the screen

for each of your nine cases as a delimiter between each one.

$5.$ For each of the three frames you have chosen, provide a brief description of your choices and

approach to indicate how your design might apply to a general case for locating instances of your

frames. you are welcome to use FrameNet, Pretty Print