Utilizing machine learning models to identify insights in a document
A device receives document information associated with a document, and receives a request to identify insights in the document information. The device performs, based on the request, natural language processing on the document information to identify words, phrases, and sentences in the document information, and utilizes a first machine learning model with the words, the phrases, and the sentences to identify information indicating abstract insights, concrete insights, and non-insights in the document. The device utilizes a second machine learning model to match the abstract insights with particular concrete insights that are different than the concrete insights, and utilizes a third machine learning model to determine particular insights based on the non-insights. The device generates an insight document that includes the concrete insights, the abstract insights matched with the particular concrete insights, and the particular insights determined based on the non-insights.
1. A method comprising:
training, by a device, a machine learning model by:
providing historical information to the machine learning model,
receiving, from the machine learning model, prediction information based on providing the historical information to the machine learning model, and
updating the machine learning model based on the prediction information;
utilizing, by the device, the trained machine learning model on a document to:
identify information indicating an abstract insight, a first concrete insight, and a non-insight in the document,
wherein the abstract insight is one or more of:
an insight that is not based on reality, a specific object, or an actual instance,
an insight that expresses a quality or characteristic apart from a specific object or an actual instance, or
a theoretical insight;
wherein the first concrete insight is one or more of:
an insight that is based on reality, a specific object, or an actual instance,
an insight pertaining to reality or an actual instance, or
an insight applied to an actual substance or thing, and
wherein the non-insight is one or more of:
an opinion,
a fact, or
a guess,
match the abstract insight with a second concrete insight that is different than the first concrete insight and the abstract insight, and
match the non-insight with a stored insight that is different from insights identified in the document;
generating, by the device, an insight document by combining the first concrete insight, the abstract insight, and the stored insight; and
providing, by the device and for display, the insight document.
2. The method of claim 1 , further comprising:
performing natural language processing on the document; and
wherein utilizing the trained machine learning model on the document comprises:
utilizing the trained machine learning model on the document based on performing the natural language processing on the document.
3. The method of claim 1 , further comprising:
utilizing a data cleansing method to filter one or more portions of the document; and
wherein utilizing the trained machine learning model on the document comprises:
utilizing the trained machine learning model on the document based on filtering the one or more portions of the document.
4. The method of claim 1 , wherein the second concrete insight is provided in a repository.
5. The method of claim 1 , wherein the stored insight is provided in a repository.
6. The method of claim 1 , further comprising:
matching the insight document with one or more other documents.
7. The method of claim 6 , further comprising:
providing the one or more other documents to another device.
8. A device, comprising:
one or more memories; and
one or more processors, coupled to the one or more memories, configured to:
provide historical information to a machine learning model;
receive, from the machine learning model, prediction information based on providing the historical information to the machine learning model;
update the machine learning model based on the prediction information;
utilize the updated machine learning model on a document to:
identify information indicating an abstract insight, a first concrete insight, and a non-insight in the document,
wherein the abstract insight is one or more of:
an insight that is not based on reality, a specific object, or an actual instance,
an insight that expresses a quality or characteristic apart from a specific object or an actual instance, or
a theoretical insight;
wherein the first concrete insight is one or more of:
an insight that is based on reality, a specific object, or an actual instance,
an insight pertaining to reality or an actual instance, or
an insight applied to an actual substance or thing, and
wherein the non-insight is one or more of:
an opinion,
a fact, or
a guess,
match the abstract insight with a second concrete insight that is different than the first concrete insight and the abstract insight, and
match the non-insight with a stored insight that is different from insights identified in the document;
generate an insight document by combining the first concrete insight, the abstract insight, and the stored insight; and
provide, for display, the insight information.
9. The device of claim 8 , wherein the one or more processors are further configured to:
perform a natural language processing technique on the document; and
wherein the one or more processors, to utilize the updated machine learning model on the document, are configured to:
utilize the updated machine learning model on the document based on performing the natural language processing technique on the document.
10. The device of claim 8 , wherein the one or more processors are further configured to:
filter one or more portions of the document utilizing a data cleansing method; and
wherein the one or more processors, to utilize the updated machine learning model on the document, are configured to:
utilize the updated machine learning model on the document based on filtering the one or more portions of the document.
11. The device of claim 8 , wherein the second concrete insight is provided in a repository.
12. The device of claim 8 , wherein the stored insight is provided in a repository.
13. The device of claim 8 , wherein the one or more processors are further configured to:
match the insight document with one or more other documents provided in a repository.
14. The device of claim 13 , wherein the one or more processors are further configured to:
provide the one or more other documents to a client device.
15. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a device, cause the device to:
provide historical information to a machine learning model;
receive, from the machine learning model, prediction information based on providing the historical information to the machine learning model;
update the machine learning model based on the prediction information;
identify, utilizing the updated machine learning model, information indicating an abstract insight, a first concrete insight, and a non-insight in a document,
wherein the abstract insight is one or more of:
an insight that is not based on reality, a specific object, or an actual instance,
an insight that expresses a quality or characteristic apart from a specific object or an actual instance, or
a theoretical insight;
wherein the first concrete insight is one or more of:
an insight that is based on reality, a specific object, or an actual instance,
an insight pertaining to reality or an actual instance, or
an insight applied to an actual substance or thing, and
wherein the non-insight is one or more of:
an opinion,
a fact, or
a guess;
match, utilizing the updated machine learning model, the abstract insight with a second concrete insight that is different than the first concrete insight and the abstract insight, and
match, utilizing the updated machine learning model, the non-insight with a stored insight that is different from insights identified in the document;
generate an insight document by combining the first concrete insight, the abstract insight, and the stored insight; and
provide, for display, the insight document.
16. The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions further cause the device to:
perform natural language processing on the document; and
wherein the one or more instructions, that cause the device to identify the information indicating the abstract insight, the first concrete insight, and the non-insight in the document, cause the device to:
identify the information indicating the abstract insight, the first concrete insight, and the non-insight in the document based on performing the natural language processing on the document.
17. The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions further cause the device to:
filter one or more portions of the document utilizing a data cleansing method; and
wherein the one or more instructions, that cause the device to identify the information indicating the abstract insight, the first concrete insight, and the non-insight in the document, cause the device to:
identify the information indicating the abstract insight, the first concrete insight, and the non-insight in the document based on filtering the one or more portions of the document.
18. The non-transitory computer-readable medium of claim 15 , wherein the second concrete insight is provided in a repository.
19. The non-transitory computer-readable medium of claim 15 , wherein the stored insight is provided in a repository.
20. The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions further cause the device to:
match the insight document with one or more other documents; and
provide the one or more other documents to another device.