Identifying tasks and commitments using natural language processing and machine learning

1. A computer-implemented method for identifying tasks and commitments, comprising:

receiving, via communication links, a communication message;

identifying a task and a parameter of the task in the communication message;

extracting information related to the task from the communication message using natural language processing (NLP) and machine learning (ML);

identifying a commitment using NLP extracted information and a state of the commitment using NLP and ML based on the extracted information;

receiving an additional communication message associated with the identified task; and

updating the state of the commitment based on information extracted from the received additional communication message.

2. The method of claim 1 , wherein extracting the information related to the task comprises extracting task owner information.

3. The method of claim 1 , comprising identifying a delegation of the commitment from one debtor to another.

4. The method of claim 1 , comprising identifying a discharge of the commitment.

5. The method of claim 1 , comprising identifying a class of the task in relation to the commitment using ML.

6. A computing system for identifying tasks and commitments comprising:

a memory resource;

a processing resource coupled to the memory resource to:

receive, via communication links, a communication message;

perform pronoun resolution and name entity resolution on information within the communication message;

extract part-of-speech tags and typed dependencies from the communication message using machine learning;

identify a task in the communication message based on the resolution and the extraction information;

identify a parameter of the task based on the resolution and the extraction information, wherein the parameter comprises at least one of:

a commitment to perform the task by either of a user sending the communication message and a user receiving the communication message;

a delegation of the commitment from one user to another user;

a discharge of the commitment; and

a class of the task in relation to the commitment using machine learning; and

display the identified task and parameter on a user platform.

7. The system of claim 6 , comprising the processing resource coupled to the memory resource to identify a task by determination of whether there is a valid subject and a valid action verb in a sentence of the communication message and association of nouns with the valid action verb based on a dependency list.

8. The system of claim 6 , wherein the commitment to perform the task is identified when an action verb associated with the identified task follows a modal verb in the communication message.

9. The system of claim 6 , wherein the delegation of the commitment is identified by designation of a first user that was a debtor as a creditor and designation of a second user as a new debtor responsible for the commitment.

10. The system of claim 6 , wherein the discharge of the commitment is identified by comparison of words in a first communication message associated with a first task that signals the commitment to words in a second communication message of a second task and determination of the commitment as discharged by the second task when:

the first and second tasks have a same user to perform the first and second tasks;

an action verb in the second communication message of the second task is past tense and action verbs in the first and second communication messages associated with the first and second tasks are related; and

nouns that correspond to the action verbs in the first and second communication messages associated with the first and second tasks are similar.

11. A non-transitory machine-readable medium storing a set of instructions for identifying tasks and commitments, wherein the set of instructions is executable by a processor to cause a computer to:

receive, via communication links, a communication message;

identify a task in the communication message based on pronoun resolution, name entity resolution (NER), and extraction of part-of-speech tags and typed dependencies from the communication message using machine learning;

identify a parameter of the task based on the pronoun and NER and the part-of-speech tags extraction information;

display the identified task and the identified parameter on a user interface; and

update the displayed task and the parameter based on user input.

12. The medium of claim 11 , comprising to update the parameter of the task based on a change in a lifecycle of the task.

13. The medium of claim 11 , wherein the parameter of the task comprises at least one of:

a commitment to perform the task by a user;

a delegation of the commitment; and

a discharge of the commitment.

14. The medium of claim 11 , wherein to update the displayed task and the parameter comprises at least one of identifying whether a task indicates a commitment and updating an already identified commitment.

15. The medium of claim 11 , wherein the pronoun resolution, NER, and extraction of part-of-speech tags are performed by a natural language parser.