In this article, we will find the most similar sentence in the file to the input sentence.
Example:
File content: "This is movie." "This is romantic movie" "This is a girl." Input: "This is a boy" Similar sentence to input: "This is a girl", "This is movie".
Approach:
- Create a list to store all the unique words of the file.
- Convert all the sentences of the file into the binary format by comparing each word with the content of the list, after cleaning(removing stopword, stemming, etc.)
- Convert the input sentence in the binary format.
- Find the number of similar words in the input sentence to each sentence and store the value in the list named similarity index.
- Find the maximum value of similarity index and return the sentence having maximum similar words.
Content of the file:
Code to get a similar sentence:
Python3
from nltk.stem import PorterStemmer from nltk.tokenize import word_tokenize, sent_tokenize import nltk from nltk.corpus import stopwords nltk.download('stopwords') ps = PorterStemmer() f = open('romyyy.txt') a = sent_tokenize(f.read()) # removal of stopwords stop_words = list(stopwords.words('english')) # removal of punctuation signs punc = '''!()-[]{};:'"\, <>./?@#$%^&*_~'''s = [(word_tokenize(a[i])) for i in range(len(a))] outer_1 = [] for i in range(len(s)): inner_1 = [] for j in range(len(s[i])): if s[i][j] not in (punc or stop_words): s[i][j] = ps.stem(s[i][j]) if s[i][j] not in stop_words: inner_1.append(s[i][j].lower()) outer_1.append(set(inner_1)) rvector = outer_1[0] for i in range(1, len(s)): rvector = rvector.union(outer_1[i]) outer = [] for i in range(len(outer_1)): inner = [] for w in rvector: if w in outer_1[i]: inner.append(1) else: inner.append(0) outer.append(inner) comparison = input("Input: ") check = (word_tokenize(comparison)) check = [ps.stem(check[i]).lower() for i in range(len(check))] check1 = [] for w in rvector: if w in check: check1.append(1) # create a vector else: check1.append(0) ds = [] for j in range(len(outer)): similarity_index = 0 c = 0 if check1 == outer[j]: ds.append(0) else: for i in range(len(rvector)): c += check1[i]*outer[j][i] similarity_index += c ds.append(similarity_index) ds maximum = max(ds) print() print() print("Similar sentences: ") for i in range(len(ds)): if ds[i] == maximum: print(a[i]) |
Output:
