Create Inverted Index for File using Python
An inverted index is an index data structure storing a mapping from content, such as words or numbers, to its locations in a document or a set of documents. In simple words, it is a hashmap like data structure that directs you from a word to a document or a web page.
Creating Inverted Index
We will create a Word level inverted index, that is it will return the list of lines in which the word is present. We will also create a dictionary in which key values represent the words present in the file and the value of a dictionary will be represented by the list containing line numbers in which they are present. To create a file in Jupiter notebook use magic function:
%%writefile file.txt
This is the first word.
This is the second text, Hello! How are you?
This is the third, this is it now.
This will create a file named file.txt will the following content.
To read file:
Python3
file = open ( 'file.txt' , encoding = 'utf8' )
read = file .read()
file .seek( 0 )
read
line = 1
for word in read:
if word = = '\n' :
line + = 1
print ( "Number of lines in file is: " , line)
array = []
for i in range (line):
array.append( file .readline())
array
|
Output:
Number of lines in file is: 3
['This is the first word.\n',
'This is the second text, Hello! How are you?\n',
'This is the third, this is it now.']
Functions used:
- Open: It is used to open the file.
- read: This function is used to read the content of the file.
- seek(0): It returns the cursor to the beginning of the file.
Remove punctuation:
Python3
punc =
for ele in read:
if ele in punc:
read = read.replace(ele, " " )
read
read = read.lower()
read
|
Output:
'this is the first word \n
this is the second text hello how are you \n
this is the third this is it now '
Tokenize the data as individual words:
Apply linguistic preprocessing by converting each words in the sentences into tokens. Tokenizing the sentences help with creating the terms for the upcoming indexing operation.
Python3
def tokenize_words(file_contents):
result = []
for i in range ( len (file_contents)):
tokenized = []
tokenized = file_contents[i].split()
result.append(tokenized)
return result
|
Clean data by removing stopwords:
Stop words are those words that have no emotions associated with it and can safely be ignored without sacrificing the meaning of the sentence.
Python3
from nltk.tokenize import word_tokenize
import nltk
from nltk.corpus import stopwords
nltk.download( 'stopwords' )
for i in range ( 1 ):
text_tokens = word_tokenize(read)
tokens_without_sw = [
word for word in text_tokens if not word in stopwords.words()]
print (tokens_without_sw)
|
Output:
['first', 'word', 'second', 'text', 'hello', 'third']
Create an inverted index:
Python3
dict = {}
for i in range (line):
check = array[i].lower()
for item in tokens_without_sw:
if item in check:
if item not in dict :
dict [item] = []
if item in dict :
dict [item].append(i + 1 )
dict
|
Output:
{'first': [1],
'word': [1],
'second': [2],
'text': [2],
'hello': [2],
'third': [3]}
Last Updated :
30 Jan, 2023
Like Article
Save Article
Share your thoughts in the comments
Please Login to comment...