Knowledge Base for Analysis of Text

• US 20180173698A1
• Filed: 04/14/2017
• Published: 06/21/2018
• Est. Priority Date: 12/16/2016
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Dubey, Rajeev, Jain, Navendu
• Granted Patent: US 10,679,008 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/3347 using vector based model
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

A knowledge base can include a dictionary associated with classes of a model, e.g., an ontology. A text segment that is not found in the dictionary can be received. Feature(s) can be determined for the text segment and, based partly on providing the feature(s) to a classifier, a set of values can be determined. The distribution can include values respectively corresponding to the classes. One of the values can be greater than a predetermined threshold. That value can correspond to a class. An indication identifying the class can be presented via a user interface having functionality to provide input that the text segment is associated with the class, is not associated with the class, or is associated with another class. Based at least partly on adding a new class to the ontology, a precedence table indicating priorities between motifs defining relationships between classes of the ontology can be updated.

Claims

1. A system comprising:

at least one processor;

memory communicatively coupled to the at least one processor; and

one or more modules stored in the memory and executable by the at least one processor to;

determine a first set of values associated with the first text segment, the first set of values including a plurality of first values respectively corresponding to individual classes of a plurality of classes of an ontology;

determine that a first value of the plurality of first values is greater than a first predetermined threshold, the first value corresponding to a first class of the plurality of classes; and

present, via a user interface, a first indication identifying the first class, the user interface configured to receive at least one of;

a first command to cause the first text segment to be mapped to a first entry in a dictionary corresponding to the first class;

or

a second command to cause the first text segment to be mapped to a second entry in the dictionary corresponding to a second class of the plurality of classes instead of the first entry.

2. The system as claim 1 recites, wherein the one or more modules stored in the memory are further executable by the at least one processor to:

determine a feature vector associated with the first text segment, wherein the feature vector comprises at least one of a parts-of-speech tag for the first text segment, stemmed output associated with the first text segment in view of other stemmed output, or a co-occurrence relationship between the first text segment and second text segments used in a similar context as the first text segment; and

determine the first set of values based on the feature vector.

3. A system as claim 1 recites, wherein the plurality of classes of the ontology comprise at least one of a suggestion-indicator class for text segment(s) that represent the presence of a suggestion, a modal-indicator class for text segment(s) that represent the presence of a suggestion, an entity class for text segment(s) that represents an entity, an action class for text segment(s) that represents a behavior that is taken on an entity, a certainty class for text segment(s) that represents the confidence of the user regarding an entity or condition represented in the text segment(s), an emotion class for text segment(s) that represents feelings or beliefs of the user writing the text segment(s), a negation class for text segment(s) that change the meaning of other text segment(s) to a substantially opposite meaning, or a qualifier class for text segment(s) that represents descriptive terms used to describe an action or a condition.

4. A system as claim 1 recites, wherein the one or more modules are further executable by the at least one processor to:

receive the first command; and

map the first text segment to the first entry corresponding to the first class in the dictionary.

5. A system as claim 1 recites, wherein the one or more modules are further executable by the at least one processor to:

receive the second command and an identification of the second class; and

map the first text segment to the second entry corresponding to the second class.

6. A system as claim 1 recites, wherein the user interface is further configured to receive a third command to prevent the first text segment from being added to the dictionary and the module is executable by the at least one processor further to:

receive the third command; and

refrain from adding the first text segment to the dictionary.

7. A system as claim 1 recites, wherein the one or more modules are further executable by the at least one processor to:

determine a second set of values associated with a second text segment, the second set of values including a plurality of second values respectively corresponding to individual classes of the plurality of classes;

determine that each second value of the plurality of second values is less than or equal to the first predetermined threshold;

determine a highest second value of the plurality of second values; and

present, via the user interface, a second indication identifying a third class of the plurality of classes, the third class corresponding to the highest second value and the user interface configured to receive at least one of;

a third command to cause the second text segment to be mapped to a third entry in the dictionary corresponding to the third class;

a fourth command to cause the second text segment to be mapped to a fourth entry in the dictionary corresponding to a fourth class of the plurality of classes instead of the third entry;

or

a fifth command to prevent the second text segment from being added to the dictionary.

8. A system as claim 1 recites, wherein the one or more modules are further executable by the at least one processor to:

determine a second set of values associated with a second text segment, the second set of values including a plurality of second values respectively corresponding to individual classes of the plurality of classes;

determine that a second value of the plurality of second values is greater than a second predetermined threshold, the second value corresponding to a third class of the plurality of classes; and

automatically map the second text segment to a third entry of the plurality of entries, the third entry corresponding to the third class.

9. A system as claim 8 recites, wherein the second predetermined threshold is associated with a greater value than the first predetermined threshold.

10. A system as claim 1 recites, wherein the one or more modules are further executable by the at least one processor to:

determine a second set of values associated with a second text segment, the second set of values including a plurality of second values respectively corresponding to individual classes of the plurality of classes;

determine that a second value of the plurality of second values is less than a second predetermined threshold, the second value corresponding to a third class of the plurality of classes; and

automatically refrain from mapping the second text segment to a third entry corresponding to the third class in the dictionary.

11. A system as claim 1 recites, wherein the one or more modules are further executable by the at least one processor to:

based at least in part on receiving input associated with the second command, determine that the second class corresponds to a new class; and

update a precedence table to provide an updated precedence table, the updated precedence table identifying at least one motif associated with the second class that is preceded by or succeeded by another motif associated with the second class.

12. A system as claim 1 recites, wherein the one or more modules are further executable by the at least one processor to:

based at least in part on receiving input associated with the second command, receive an indication that the second class is associated with a positive sub-class, a negative sub-class, or a neutral sub-class; and

map the first text segment to the second class and the positive sub-class, the negative sub-class, or the neutral sub-class.

13. A system as claim 1 recites, wherein the one or more modules are further executable by the at least one processor to:

receive, from a computing device operated by a user, a new text segment;

determine, based at least in part on the dictionary, that the new text segment is associated with the second class; and

infer an actionable item associated with the new text segment based at least in part on the second class and at least one grammar pattern.

14. A computer-implemented method comprising:

receiving, from a computing device operated by a user, a document associated with a text segment;

accessing a predetermined knowledge base including;

a dictionary associated with an ontology comprising a plurality of classes; and

a precedence table, the precedence table establishing a priority between motifs;

determining, based at least in part on the dictionary, that the text segment is associated with a first motif and a second motif, the first motif defining a first relationship between individual classes of the plurality of classes and the second motif defining a second relationship between individual classes of the plurality of classes;

determining, based at least in part on the precedence table, that the first motif precedes the second motif; and

inferring an actionable item associated with the text segment based at least in part on the first motif.

15. A computer-implemented method as claim 14 recites, further comprising:

determining, based at least in part on the dictionary, that the text segment is associated with a third motif defining a third relationship between individual classes of the plurality of classes; and

determining, based at least in part on the precedence table, that the first motif precedes the third motif and that the third motif precedes the second motif.

16. A computer-implemented method as claim 14 recites, further comprising:

determining, based at least in part on the dictionary, that the text segment is associated with a third motif defining a third relationship between individual classes of the plurality of classes; and

determining, based at least in part on the precedence table, that the second motif precedes the third motif.

17. A computer-implemented method as claim 14 recites, further comprising, prior to receiving the document associated with the text segment, generating the precedence table based at least in part on:

associating individual motifs with values representative of importance; and

determining prioritizations between the individual motifs based at least in part on the values.

18. A computer-implemented method as claim 14 recites, further comprising:

updating the dictionary based at least in part on;

receiving a new text segment;

determining a first set of values associated with the new text segment, the first set of values including a plurality of first values respectively corresponding to individual classes of the plurality of classes;

determining that a first value of the plurality of first values is greater than a first predetermined threshold, the first value corresponding to a particular class of the plurality of classes;

presenting, via a user interface, a first indication identifying the particular class, the user interface configured to receive at least one of;

a first command to cause the first text segment to be mapped to a first entry in the dictionary corresponding to the particular class;

or

a second command to cause the first text segment to be mapped to a second entry in the dictionary corresponding to a different class of the plurality of classes instead of the first entry; and

receiving input corresponding to the second command; and

updating the precedence table based at least in part on the input.

19. One or more computer readable media storing computer-executable instructions that, when executed by the at least one processor, cause the at least one processor to perform acts comprising:

receiving, from a computing device operated by a user, a document associated with a text segment;

accessing a predetermined knowledge base including a dictionary associated with an ontology comprising a plurality of classes, a class of the plurality of classes being associated with a positive sub-class, a negative sub-class, and a neutral-subclass;

determining, based at least in part on the ontology, that the text segment is associated with the positive sub-class, the negative sub-class, or the neutral-subclass; and

inferring an actionable item associated with the text segment based at least in part on the positive sub-class, the negative sub-class, or the neutral-subclass and at least one grammar pattern.

20. One or more computer readable media as claim 19 recites, the acts further comprising updating the dictionary based at least in part on:

receiving a new text segment;

determining a first set of values associated with the new text segment, the first set of values including a plurality of first values respectively corresponding to individual classes of the plurality of classes;

determining that a first value of the plurality of first values is greater than a first predetermined threshold, the first value corresponding to a particular class of the plurality of classes;

presenting, via a user interface, a first indication identifying the particular class, the user interface configured to receive at least one of;

a first command to cause the first text segment to be mapped to a first entry in the dictionary corresponding to the particular class;

or

a second command to cause the first text segment to be mapped to a second entry in the dictionary corresponding to a different class of the plurality of classes instead of the first entry; and

receiving input corresponding to the first command or the second command.