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I am working on a project where I need to find similar roles -- for example, Software Engineer, Soft. Engineer , Software Eng ( all should be marked similar)

Currently, I have tried using the Standard Occupational Classification Dataset and tried using LSA, Leveinstein and unsupervised FastText with Word Movers Distances. The last option works but isn't great.

I am wondering if there are more comprehensive data sets or ways available to solve this problem?? Any lead would be helpful!

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1 Answer 1

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You can calculate the text similarity using Transformers. With transformers, we can get better accuracies. Try the following code:

pip install sentence-transformers==1.2.1

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-uncased')

sen = [
"Software Engineer", 
"Soft. Engineer" , 
"Software Eng",
"Senior Software Engineer",
]

sen_embeddings = model.encode(sen)

from sklearn.metrics.pairwise import cosine_similarity
#let's calculate cosine similarity for sentence 0:
cosine_similarity(
    [sen_embeddings[0]],
    sen_embeddings[1:]
)

If the similarity score is greater than 0.6 ( or 0.7), you can assume the texts to be similar.

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  • $\begingroup$ This doesn't work, almost all words give a similarity score of 0.7+ ( closer to 0.9) $\endgroup$
    – rspenpal
    Aug 23, 2021 at 7:21

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