Suggestions / refer links /codes are appreciated.
I have a data which is having more than 1500 rows. Each row has a sentence. I am trying to find out the best method to find the most similar sentences among all.
What I have tried
I have tried K-mean algorithm which groups similar sentences in a cluster. But I found a drawback in which I have to pass K to create a cluster. It is hard to guess K. I tried elbo method to guess the clusters but grouping all together isn't sufficient. In this approach I am getting all the data grouped. I am looking for data which is similar above 0.90% data should be returned with ID.
I tried cosine similarity in which I used TfidfVectorizer to create matrix and then passed in cosine similarity. Even this approach didn't worked properly.
What I am looking for
I want an approach where I can pass a threshold example 0.90 data in all rows which are similar to each other above 0.90% should be returned as a result.
Data Sample
ID | DESCRIPTION
-----------------------------
10 | Cancel ASN WMS Cancel ASN
11 | MAXPREDO Validation is corect
12 | Move to QC
13 | Cancel ASN WMS Cancel ASN
14 | MAXPREDO Validation is right
15 | Verify files are sent every hours for this interface from Optima
16 | MAXPREDO Validation are correct
17 | Move to QC
18 | Verify files are not sent
Expected result
Above data which are similar upto 0.90% should get as a result with ID
ID | DESCRIPTION
-----------------------------
10 | Cancel ASN WMS Cancel ASN
13 | Cancel ASN WMS Cancel ASN
11 | MAXPREDO Validation is corect # even spelling is not correct
14 | MAXPREDO Validation is right
16 | MAXPREDO Validation are correct
12 | Move to QC
17 | Move to QC
Why did it not work for you with cosine similarity and the TFIDF-vectorizer?
I tried it and it works with this code:
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
df = pd.DataFrame(columns=["ID","DESCRIPTION"], data=np.matrix([[10,"Cancel ASN WMS Cancel ASN"],
[11,"MAXPREDO Validation is corect"],
[12,"Move to QC"],
[13,"Cancel ASN WMS Cancel ASN"],
[14,"MAXPREDO Validation is right"],
[15,"Verify files are sent every hours for this interface from Optima"],
[16,"MAXPREDO Validation are correct"],
[17,"Move to QC"],
[18,"Verify files are not sent"]
]))
corpus = list(df["DESCRIPTION"].values)
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
threshold = 0.4
for x in range(0,X.shape[0]):
for y in range(x,X.shape[0]):
if(x!=y):
if(cosine_similarity(X[x],X[y])>threshold):
print(df["ID"][x],":",corpus[x])
print(df["ID"][y],":",corpus[y])
print("Cosine similarity:",cosine_similarity(X[x],X[y]))
print()
The threshold can be adjusted as well, but will not yield the results you want with a threshold of 0.9.
The output for a threshold of 0.4 is:
10 : Cancel ASN WMS Cancel ASN
13 : Cancel ASN WMS Cancel ASN
Cosine similarity: [[1.]]
11 : MAXPREDO Validation is corect
14 : MAXPREDO Validation is right
Cosine similarity: [[0.64183024]]
12 : Move to QC
17 : Move to QC
Cosine similarity: [[1.]]
15 : Verify files are sent every hours for this interface from Optima
18 : Verify files are not sent
Cosine similarity: [[0.44897995]]
With a threshold of 0.39 all your expected sentences are features in the output, but an additional pair with the indices [15,18] can be found as well:
10 : Cancel ASN WMS Cancel ASN
13 : Cancel ASN WMS Cancel ASN
Cosine similarity: [[1.]]
11 : MAXPREDO Validation is corect
14 : MAXPREDO Validation is right
Cosine similarity: [[0.64183024]]
11 : MAXPREDO Validation is corect
16 : MAXPREDO Validation are correct
Cosine similarity: [[0.39895808]]
12 : Move to QC
17 : Move to QC
Cosine similarity: [[1.]]
14 : MAXPREDO Validation is right
16 : MAXPREDO Validation are correct
Cosine similarity: [[0.39895808]]
15 : Verify files are sent every hours for this interface from Optima
18 : Verify files are not sent
Cosine similarity: [[0.44897995]]