问题描述
限时送ChatGPT账号..建议/参考链接/代码表示赞赏.
Suggestions / refer links /codes are appreciated.
我有一个超过 1500 行的数据.每一行都有一个句子.我正在尝试找出在所有句子中找到最相似句子的最佳方法.
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.
我的尝试
我尝试了 K-mean 算法,该算法将相似的句子分组在一个集群中.但是我发现了一个缺点,我必须通过 K 来创建一个集群.很难猜到K.我尝试了 elbo 方法来猜测集群,但将所有组合在一起是不够的.在这种方法中,我将所有数据分组.我正在寻找与 0.90% 以上的数据类似的数据,应返回 ID.
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.
我尝试了余弦相似度,其中我使用 TfidfVectorizer
创建矩阵,然后传入余弦相似度.即使这种方法也不能正常工作.
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.
我在找什么
我想要一种方法,我可以在其中传递一个阈值示例 0.90 数据应作为结果返回.
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
预期结果
使用 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
推荐答案
为什么余弦相似度和 TFIDF 向量化器对您不起作用?
Why did it not work for you with cosine similarity and the TFIDF-vectorizer?
我试过了,它适用于以下代码:
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()
阈值也可以调整,但阈值为 0.9 不会产生您想要的结果.
The threshold can be adjusted as well, but will not yield the results you want with a threshold of 0.9.
阈值为 0.4 的输出为:
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]]
阈值为 0.39 时,您所有预期的句子都是输出中的特征,但也可以找到带有索引 [15,18] 的附加对:
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]]
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