Match a two items from two different receipts
$begingroup$
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.
clustering similarity distance text cosine-distance
asked 11 mins ago
Kavita AherwarKavita Aherwar
1
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Kavita Aherwar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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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.
clustering similarity distance text cosine-distance
asked 11 mins ago
Kavita AherwarKavita Aherwar
1
New contributor
Kavita Aherwar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
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.
clustering similarity distance text cosine-distance
asked 11 mins ago
Kavita AherwarKavita Aherwar
1
New contributor
Kavita Aherwar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
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.
clustering similarity distance text cosine-distance
clustering similarity distance text cosine-distance
asked 11 mins ago
Kavita AherwarKavita Aherwar
1
New contributor
Kavita Aherwar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 11 mins ago
Kavita AherwarKavita Aherwar
1
New contributor
Kavita Aherwar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 11 mins ago
Kavita AherwarKavita Aherwar
1
New contributor
Kavita Aherwar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 11 mins ago
Kavita AherwarKavita Aherwar
1
asked 11 mins ago
Kavita AherwarKavita Aherwar
1
1
New contributor
Kavita Aherwar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Kavita Aherwar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Kavita Aherwar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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