(TF-IDF)计算余弦相似度后如何返回五篇相关文章

Question

I get a dataframe sample_df (4 columns: paper_id , title , abstract , body_text ). I extracted the abstract column(~1000 words per abstract) and apply the text cleaning process. Here's my question:

After finished calculating the cosine similarity between question and abstract, how can it return the top5 articles score with corresponding information(eg paper_id , title , body_text ) since my goal is to do tf -idf question answering.

I'm really sorry that my english is poor and I am new to nlp. I would appreciated if someone can help.

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

First: if you want 5 articles then instead of [:-5:-1] you have to use [:-6:-1] because for negative values it works little different.

Or use [::-1][:5] - [::-1] will reverse all values and then you can use normal [:5]

---

When you have related_docs_indices then you can use .iloc[] to get elements from DataFrame

 sample_df.iloc[ related_docs_indices ]

If you will have elements with the same similarity then it will gives them in reversed order.

---

BTW:

You can also add similarities to DataFrame

sample_df['similarity'] = cosine_similarities

and then sort (reversed) and get 5 items.

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

If you will have elements with the same similarity then it will gives them in original order.

---

Minimal working code with some data - so everyone can copy and test it.

Because I have only 5 elements in DataFrame so I search 2 elements.

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] )

Result:

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