Named Entity Recognition (NER) / Matching with a List of Entities
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For a particular task, I need to identify named entities in PDF files. The classification is binary since I have only one type of entity to recognize. These entities are product names for which I have a non-exhaustive list. My current approach is the following:
1) use an exact match algorithm to extract from the PDF the entities that I know from the list. This should lead to a good precision but a poor recall.
2) learn a NER model from the extracted exact match. Hopefully, this would lead to a better recall.
I have two question about this approach:
- Do you know a similar dataset ? (traditional NER dataset + known list of entities)
- How would you take profit from the given list of entities to achieve better results ? Regular NER tasks often have only example of text-entities relations. In my case, I intend to get such relations with step 1 and benefit from the fact that I know the correct spelling of several entities I want to find.
named-entity-recognition
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$begingroup$
For a particular task, I need to identify named entities in PDF files. The classification is binary since I have only one type of entity to recognize. These entities are product names for which I have a non-exhaustive list. My current approach is the following:
1) use an exact match algorithm to extract from the PDF the entities that I know from the list. This should lead to a good precision but a poor recall.
2) learn a NER model from the extracted exact match. Hopefully, this would lead to a better recall.
I have two question about this approach:
- Do you know a similar dataset ? (traditional NER dataset + known list of entities)
- How would you take profit from the given list of entities to achieve better results ? Regular NER tasks often have only example of text-entities relations. In my case, I intend to get such relations with step 1 and benefit from the fact that I know the correct spelling of several entities I want to find.
named-entity-recognition
$endgroup$
add a comment |
$begingroup$
For a particular task, I need to identify named entities in PDF files. The classification is binary since I have only one type of entity to recognize. These entities are product names for which I have a non-exhaustive list. My current approach is the following:
1) use an exact match algorithm to extract from the PDF the entities that I know from the list. This should lead to a good precision but a poor recall.
2) learn a NER model from the extracted exact match. Hopefully, this would lead to a better recall.
I have two question about this approach:
- Do you know a similar dataset ? (traditional NER dataset + known list of entities)
- How would you take profit from the given list of entities to achieve better results ? Regular NER tasks often have only example of text-entities relations. In my case, I intend to get such relations with step 1 and benefit from the fact that I know the correct spelling of several entities I want to find.
named-entity-recognition
$endgroup$
For a particular task, I need to identify named entities in PDF files. The classification is binary since I have only one type of entity to recognize. These entities are product names for which I have a non-exhaustive list. My current approach is the following:
1) use an exact match algorithm to extract from the PDF the entities that I know from the list. This should lead to a good precision but a poor recall.
2) learn a NER model from the extracted exact match. Hopefully, this would lead to a better recall.
I have two question about this approach:
- Do you know a similar dataset ? (traditional NER dataset + known list of entities)
- How would you take profit from the given list of entities to achieve better results ? Regular NER tasks often have only example of text-entities relations. In my case, I intend to get such relations with step 1 and benefit from the fact that I know the correct spelling of several entities I want to find.
named-entity-recognition
named-entity-recognition
asked 2 days ago
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