Using serial machine learning models to extract data from electronic documents

1. A method comprising:

receiving a document having a plurality of first text strings each representing a respective description related to a real estate transaction, wherein each first text string has a corresponding value;

extracting the plurality of first text strings from the document;

providing the extracted plurality of first text strings to a first machine learning model, wherein the first machine learning model is trained to output a numerical vector representation for each input first text string, wherein each vector representation represents a meaning of the corresponding description;

providing the output vector representations from the first machine learning model to a second machine learning model, wherein the second machine learning model is trained to output a second text string representing a respective title production system description related to the real estate transaction for each input vector representation such that for each description corresponding to an input first text string a corresponding title production system description is output; and

processing the second text strings to generate an output including associating the value for each first text string with the corresponding second text string.

2. The method of claim 1 , wherein the first machine learning model is a document to vector model trained using a collection of text strings to generate numerical vectors that represent the meaning of the text strings.

3. The method of claim 2 , wherein the second machine learning model is a classification model trained using a collection of vectors generated by the first machine learning model to generate a prediction that the vector corresponds to a particular title production system description of a set of title production system descriptions.

4. The method of claim 1 , wherein generating an output comprises:

comparing values associated with the second text strings with stored values; and

in response to determining one or more values that do not match, generating an error notification.

5. The method of claim 1 , wherein generating an output comprises:

inserting the second text strings into a second document;

inserting corresponding values for each of the second text strings and extracted from the received document into the second document; and

providing the second document to a sender of the received document.

6. The method of claim 1 , comprising, validating the values corresponding to the respective descriptions of the plurality of first text strings in the document, the validating comprising comparing a control value extracted from the document and associated with the real estate transaction with a corresponding stored value.

7. The method of claim 1 , wherein generating the output comprises generating one or more fee descriptions of a title production system corresponding to the first text strings, and wherein the method further comprises performing data alignment comparing values associated with the fee descriptions with stored values for the same fee descriptions in the title production system for the real estate transaction.

8. A system comprising:

one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:

receiving a document having a plurality of first text strings each representing a respective description related to a real estate transaction, wherein each first text string has a corresponding value;

extracting the plurality of first text strings from the document;

providing the extracted plurality of first text strings to a first machine learning model, wherein the first machine learning model is trained to output a numerical vector representation for each input first text string, wherein each vector representation represents a meaning of the corresponding description;

providing the output vector representations from the first machine learning model to a second machine learning model, wherein the second machine learning model is trained to output a second text string representing a respective title production system description related to the real estate transaction for each input vector representation such that for each description corresponding to an input first text string a corresponding title production system description is output; and

processing the second text strings to generate an output including associating the value for each first text string with the corresponding second text string.

9. The system of claim 8 , wherein the first machine learning model is a document to vector model trained using a collection of text strings to generate numerical vectors that represent the meaning of the text strings.

10. The system of claim 9 , wherein the second machine learning model is a classification model trained using a collection of vectors generated by the first machine learning model to generate a prediction that the vector corresponds to a particular title production system description of a set of title production system descriptions.

11. The system of claim 8 , wherein generating an output comprises:

comparing values associated with the second text strings with stored values; and

in response to determining one or more values that do not match, generating an error notification.

12. The system of claim 8 , wherein generating an output comprises:

inserting the second text strings into a second document;

inserting corresponding values for each of the second text strings and extracted from the received document into the second document; and

providing the second document to a sender of the received document.

13. The system of claim 8 , comprising, validating the values corresponding to the respective descriptions of the plurality of first text strings in the document, the validating comprising comparing a control value extracted from the document and associated with the real estate transaction with a corresponding stored value.

14. The system of claim 8 , wherein generating the output comprises generating one or more fee descriptions of a title production system corresponding to the first text strings, and wherein the operations further comprise performing data alignment comparing values associated with the fee descriptions with stored values for the same fee descriptions in the title production system for the real estate transaction.

15. One or more non-transitory computer storage media encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

receiving a document having a plurality of first text strings each representing a respective description related to a real estate transaction, wherein each first text string has a corresponding value;

extracting the plurality of first text strings from the document;

providing the extracted plurality of first text strings to a first machine learning model, wherein the first machine learning model is trained to output a numerical vector representation for each input first text string, wherein each vector representation represents a meaning of the corresponding description;

providing the output vector representations from the first machine learning model to a second machine learning model, wherein the second machine learning model is trained to output a second text string representing a respective title production system description related to the real estate transaction for each input vector representation such that for each description corresponding to an input first text string a corresponding title production system description is output; and

processing the second text strings to generate an output including associating the value for each first text string with the corresponding second text string.

16. The non-transitory computer storage media of claim 15 , wherein the first machine learning model is a document to vector model trained using a collection of text strings to generate numerical vectors that represent the meaning of the text strings.

17. The non-transitory computer storage media of claim 16 , wherein the second machine learning model is a classification model trained using a collection of vectors generated by the first machine learning model to generate a prediction that the vector corresponds to a particular title production system description of a set of title production system descriptions.

18. The non-transitory computer storage media of claim 15 , wherein generating an output comprises:

comparing values associated with the second text strings with stored values; and

in response to determining one or more values that do not match, generating an error notification.

19. The non-transitory computer storage media of claim 15 , wherein generating an output comprises:

inserting the second text strings into a second document;

inserting corresponding values for each of the second text strings and extracted from the received document into the second document; and

providing the second document to a sender of the received document.

20. The non-transitory computer storage media of claim 15 , comprising, validating the values corresponding to the respective descriptions of the plurality of first text strings in the document, the validating comprising comparing a control value extracted from the document and associated with the real estate transaction with a corresponding stored value.

21. The non-transitory computer storage media of claim 15 , wherein generating the output comprises generating one or more fee descriptions of a title production system corresponding to the first text strings, and wherein the operations further comprise performing data alignment comparing values associated with the fee descriptions with stored values for the same fee descriptions in the title production system for the real estate transaction.