###Run this cell. Do not change the code in this cell from nltk.tokenize import sent_tokenize, word_tokenize from nltk.corpus import gutenberg def get_rawtext(filename='carroll-alice.txt'): text=gutenberg.raw(filename) return text def get_text(filename='carroll-alice.txt'): text=gutenberg.raw(filename) sentences=sent_tokenize(text) tokenized= [word_tokenize(sent.lower()) for sent in sentences] normalised=[["Nth" if (token.endswith( ("nd","st","th")) and token [:-2].isdigit()) else token for token in sent] for sent in tokenized] normalised=[["NUM" if token.isdigit() else token for token in sent] for sent in normalised] filtered=[[word for word in sent if word.isalpha()] for sent in normalised] return filtered

###Run this cell. Do not change the code in this cell from nltk.tokenize import sent_tokenize, word_tokenize from nltk.corpus import gutenberg def get_rawtext(filename='carroll-alice.txt'): text=gutenberg.raw(filename) return text def get_text(filename='carroll-alice.txt'): text=gutenberg.raw(filename) sentences=sent_tokenize(text) tokenized= [word_tokenize(sent.lower()) for sent in sentences] normalised=[["Nth" if (token.endswith( ("nd","st","th")) and token [:-2].isdigit()) else token for token in sent] for sent in tokenized] normalised=[["NUM" if token.isdigit() else token for token in sent] for sent in normalised] filtered=[[word for word in sent if word.isalpha()] for sent in normalised] return filtered

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Lab 14: Processing a json file Read in a JSON file such as the following: { "students":[ } { }, { "firstName" "lastName : "Doe", "major" : "CENT", "credits": 12, : "Jane", "exams" : [92.7, 91.4, 95.2] "firstName" : "John" "lastName' : "Doe" "major": "PHYS", "credits" : 10.0, "exams : [91.1, 81.3, 85.6] "firstName" : "Bill", "lastName : "Gates", "major": "ICS", "credits" : 7, exams" : [98.7, 94.4, 96.4] PART 1: Parse the JSON data into a dictionary and output the data as follows: Jane Doe is majoring in CENT John Doe is majoring in PHYS Bill Gates is majoring in ICS