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Match a two items from two different receipts
The Next CEO of Stack Overflow2019 Community Moderator Electionsimilarity measure with two featuresMahalanobis distance between two clustersJaccard similarity between two itemsIs there a way to measure correlation between two similar datasets?Is there an algorithm or NN to match two documents, basically not closely similar?Finding similarity between two histogram plotsWhat is the efficient way to generate a similarity score when comparing two face images?Euclidean distance for more than two datapointsProximity distance between two stringscosine similarity between items (purchase data) and normalisation
$begingroup$
I have two different invoices or receipts. One is a Purchase order one is something like a receipt(acknowledgement).
Suppose I have ordered(PO) Wine:
- White Wine
- Red Wine
- Rose Wine
And I receive the acknowledgement as:
- Wine Red Jacobs Creek
- White Wine
- Winter's Hill Estate Dry Rose
I want to match the strings (items) in the Purchase Order and the Invoice.
Can you suggest me ways to do it.
I have tried vectorization using Count Vectorization Alg
Then have used distance measures to calculate similarity using:
'dice', 'rogerstanimoto', 'yule', 'hamming', 'jaccard', 'braycurtis', 'canberra', 'cityblock', 'correlation', 'cosine', 'euclidean', and 'minkowski'
The problem is the position of Words.
Red Wine is will not be similar to Wine Red. But that should not be the case.
I have tried Word2Vec Algorithm too but as this is not language technically just Nouns. It did not work.
similarity distance text cosine-distance
$endgroup$
add a comment |
$begingroup$
I have two different invoices or receipts. One is a Purchase order one is something like a receipt(acknowledgement).
Suppose I have ordered(PO) Wine:
- White Wine
- Red Wine
- Rose Wine
And I receive the acknowledgement as:
- Wine Red Jacobs Creek
- White Wine
- Winter's Hill Estate Dry Rose
I want to match the strings (items) in the Purchase Order and the Invoice.
Can you suggest me ways to do it.
I have tried vectorization using Count Vectorization Alg
Then have used distance measures to calculate similarity using:
'dice', 'rogerstanimoto', 'yule', 'hamming', 'jaccard', 'braycurtis', 'canberra', 'cityblock', 'correlation', 'cosine', 'euclidean', and 'minkowski'
The problem is the position of Words.
Red Wine is will not be similar to Wine Red. But that should not be the case.
I have tried Word2Vec Algorithm too but as this is not language technically just Nouns. It did not work.
similarity distance text cosine-distance
$endgroup$
add a comment |
$begingroup$
I have two different invoices or receipts. One is a Purchase order one is something like a receipt(acknowledgement).
Suppose I have ordered(PO) Wine:
- White Wine
- Red Wine
- Rose Wine
And I receive the acknowledgement as:
- Wine Red Jacobs Creek
- White Wine
- Winter's Hill Estate Dry Rose
I want to match the strings (items) in the Purchase Order and the Invoice.
Can you suggest me ways to do it.
I have tried vectorization using Count Vectorization Alg
Then have used distance measures to calculate similarity using:
'dice', 'rogerstanimoto', 'yule', 'hamming', 'jaccard', 'braycurtis', 'canberra', 'cityblock', 'correlation', 'cosine', 'euclidean', and 'minkowski'
The problem is the position of Words.
Red Wine is will not be similar to Wine Red. But that should not be the case.
I have tried Word2Vec Algorithm too but as this is not language technically just Nouns. It did not work.
similarity distance text cosine-distance
$endgroup$
I have two different invoices or receipts. One is a Purchase order one is something like a receipt(acknowledgement).
Suppose I have ordered(PO) Wine:
- White Wine
- Red Wine
- Rose Wine
And I receive the acknowledgement as:
- Wine Red Jacobs Creek
- White Wine
- Winter's Hill Estate Dry Rose
I want to match the strings (items) in the Purchase Order and the Invoice.
Can you suggest me ways to do it.
I have tried vectorization using Count Vectorization Alg
Then have used distance measures to calculate similarity using:
'dice', 'rogerstanimoto', 'yule', 'hamming', 'jaccard', 'braycurtis', 'canberra', 'cityblock', 'correlation', 'cosine', 'euclidean', and 'minkowski'
The problem is the position of Words.
Red Wine is will not be similar to Wine Red. But that should not be the case.
I have tried Word2Vec Algorithm too but as this is not language technically just Nouns. It did not work.
similarity distance text cosine-distance
similarity distance text cosine-distance
edited 1 hour ago
Anony-Mousse
5,045625
5,045625
asked Feb 28 at 6:55
Kavita AherwarKavita Aherwar
11
11
add a comment |
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