(TF-IDF)如何return五篇相关文章计算余弦相似度后

Question

我得到一个数据框 sample_df（4 列：paper_id，标题,摘要,body_text)。我提取了摘要栏（每个摘要约 1000 个单词）并应用文本清理过程。这是我的问题：

计算完question和abstract的余弦相似度后，如何returntop5文章得分对应的信息(如paper_id,title,body_text) 因为我的目标是做 tf -idf 问答。

真的很抱歉我的英语不好，我是nlp的新手。如果有人能提供帮助，我将不胜感激。

from sklearn.metrics.pairwise import linear_kernel
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.metrics.pairwise import cosine_similarity  

txt_cleaned = get_cleaned_text(sample_df,sample_df['abstract'])
question = ['Can covid19 transmit through air']

tfidf_vector = TfidfVectorizer()

tfidf = tfidf_vector.fit_transform(txt_cleaned)

tfidf_question = tfidf_vector.transform(question)
cosine_similarities = linear_kernel(tfidf_question,tfidf).flatten()

related_docs_indices = cosine_similarities.argsort()[:-5:-1]
cosine_similarities[related_docs_indices]

#output([0.18986527, 0.18339485, 0.14951123, 0.13441914])

Answer 1

首先：如果您想要 5 篇文章，那么您必须使用 [:-6:-1] 而不是 [:-5:-1]，因为对于负值，它的工作方式略有不同。

或使用[::-1][:5] - [::-1]将反转所有值然后你可以使用正常[:5]

当你有 related_docs_indices 时，你可以使用 .iloc[] 从 DataFrame

中获取元素

 sample_df.iloc[ related_docs_indices ]

如果你有相似度相同的元素，那么它会以相反的顺序给出它们。

顺便说一句：

您还可以将 similarities 添加到 DataFrame

sample_df['similarity'] = cosine_similarities

然后排序（反向）得到 5 项。

sample_df.sort_values('similarity', ascending=False)[:5]

如果你有相似度相同的元素，那么它会按原来的顺序给出它们。

带有一些数据的最小工作代码 - 所以每个人都可以复制和测试它。

因为我在DataFrame中只有5个元素，所以我搜索了2个元素。

from sklearn.metrics.pairwise import linear_kernel
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.metrics.pairwise import cosine_similarity  

import pandas as pd

sample_df = pd.DataFrame({
    'paper_id': [1, 2, 3, 4, 5],
    'title': ['Covid19', 'Flu', 'Cancer', 'Covid19 Again', 'New Air Conditioners'],
    'abstract': ['covid19', 'flu', 'cancer', 'covid19', 'air conditioner'],
    'body_text': ['Hello covid19', 'Hello flu', 'Hello cancer', 'Hello covid19 again', 'Buy new air conditioner'],
})

def get_cleaned_text(df, row):
    return row

txt_cleaned = get_cleaned_text(sample_df, sample_df['abstract'])
question = ['Can covid19 transmit through air']

tfidf_vector = TfidfVectorizer()

tfidf = tfidf_vector.fit_transform(txt_cleaned)

tfidf_question = tfidf_vector.transform(question)
cosine_similarities = linear_kernel(tfidf_question,tfidf).flatten()

sample_df['similarity'] = cosine_similarities

number = 2
#related_docs_indices = cosine_similarities.argsort()[:-(number+1):-1]
related_docs_indices = cosine_similarities.argsort()[::-1][:number]

print('index:', related_docs_indices)

print('similarity:', cosine_similarities[related_docs_indices])

print('\n--- related_docs_indices ---\n')

print(sample_df.iloc[related_docs_indices])

print('\n--- sort_values ---\n')

print( sample_df.sort_values('similarity', ascending=False)[:number] )

结果：

index: [3 0]
similarity: [0.62791376 0.62791376]

--- related_docs_indices ---

   paper_id          title abstract            body_text  similarity
3         4  Covid19 Again  covid19  Hello covid19 again    0.627914
0         1        Covid19  covid19        Hello covid19    0.627914

--- sort_values ---

   paper_id          title abstract            body_text  similarity
0         1        Covid19  covid19        Hello covid19    0.627914
3         4  Covid19 Again  covid19  Hello covid19 again    0.627914

(TF-IDF)如何return五篇相关文章计算余弦相似度后

(TF-IDF)How to return the five related article after calculating cosine similarity

python

nlp

tf-idf

question-answering