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(e.g. 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])
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