Extract The Name Of Candidate From Text File Using Python And Nltk

import re import spacy import nltk from nltk.corpus import stopwords stop = stopwords.words('english') from nltk.corpus import wordnet inputfile = open('inputfile.txt', 'r') Strin

Solution 1:

import re
import nltk
from nltk.corpus import stopwords
stop = stopwords.words('english')
from nltk.corpus import wordnet

String = 'Ravana was killed in a war'

Sentences = nltk.sent_tokenize(String)
Tokens = []
for Sent in Sentences:
    Tokens.append(nltk.word_tokenize(Sent)) 
Words_List = [nltk.pos_tag(Token) for Token in Tokens]

Nouns_List = []

for List in Words_List:
    for Word in List:
        if re.match('[NN.*]', Word[1]):
             Nouns_List.append(Word[0])

Names = []
for Nouns in Nouns_List:
    if not wordnet.synsets(Nouns):
        Names.append(Nouns)

print (Names)

Check this code. I am getting Ravana as output.

EDIT:

I used a few sentences from my resume to create a text file, and gave it as input to my program. Only the changed portion of the code is shown below:

import io

File = io.open("Documents\\Temp.txt", 'r', encoding = 'utf-8')
String = File.read()
String = re.sub('[/|.|@|%|\d+]', '', String)

And it is returning all the names that are not in the wordnet corpus, like my name, my house name, place, college name and place.

Solution 2:

From the word list obtained after parts-of-speech tagging, extract all the words having noun tag using regular expression:

Nouns_List = []

for Word in nltk.pos_tag(Words_List):
    if re.match('[NN.*]', Word[1]):
         Nouns_List.append(Word[0])

For each word in the Nouns_List, check whether it is an English word. This can be done by checking whether synsets are available for that word in wordnet:

from nltk.corpus import wordnet

Names = []
for Nouns in Nouns_List:
    ifnot wordnet.synsets(Nouns):
        #Not an English word
        Names.append(Nouns)

Since Indian names cannot be entries in English dictionary, this can be a possible method to extract them from a text.