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GeorgeOfTheRF
GeorgeOfTheRF
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I have developed a NER model to detect all address and property price independently in a pdf document which have property address and its prices in natural language. There are lots of variations in how property address and prices are mentioned. It could be in described sentencse or sometimes like and many more

One possibility

address 1 details about address 1 details about address 1 price 1 address 2 details about address 2 details about address 2 price 2

So the model in a document would predict say 5 different address and 5 different property prices.

enter image description here

enter image description here

Questions

  1. Now how to build model to assign the price to the correct address?
  2. How to encode this link in the training data and learn that?

I have developed a NER model to detect all address and property price independently in a pdf document which have property address and its prices in natural language. There are lots of variations in how property address and prices are mentioned. It could be in described sentencse or sometimes like and many more

address 1 details about address 1 details about address 1 price address 2 details about address 2 details about address 2 price 2

So the model in a document would predict say 5 different address and 5 different property prices.

enter image description here

enter image description here

Questions

  1. Now how to build model to assign the price to the correct address?
  2. How to encode this link in the training data and learn that?

I have developed a NER model to detect all address and property price independently in a pdf document which have property address and its prices in natural language. There are lots of variations in how property address and prices are mentioned. It could be in described sentencse or sometimes like and many more

One possibility

address 1 details about address 1 details about address 1 price 1 address 2 details about address 2 details about address 2 price 2

So the model in a document would predict say 5 different address and 5 different property prices.

enter image description here

enter image description here

Questions

  1. Now how to build model to assign the price to the correct address?
  2. How to encode this link in the training data and learn that?
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GeorgeOfTheRF
GeorgeOfTheRF
  • 2.1k
  • 5
  • 18
  • 20

How to link/connectrelate predicted entities in named entity recognition?

Source Link
GeorgeOfTheRF
GeorgeOfTheRF
  • 2.1k
  • 5
  • 18
  • 20

How to link/connect predicted entities in named entity recognition?

I have developed a NER model to detect all address and property price independently in a pdf document which have property address and its prices in natural language. There are lots of variations in how property address and prices are mentioned. It could be in described sentencse or sometimes like and many more

address 1 details about address 1 details about address 1 price address 2 details about address 2 details about address 2 price 2

So the model in a document would predict say 5 different address and 5 different property prices.

enter image description here

enter image description here

Questions

  1. Now how to build model to assign the price to the correct address?
  2. How to encode this link in the training data and learn that?