System and method for using semantic and syntactic graphs for utterance classification
View Patent ↗Disclosed herein is a system, method and computer readable medium storing instructions related to semantic and syntactic information in a language understanding system. The method embodiment of the invention is a method for classifying utterances during a natural language dialog between a human and a computing device. The method comprises receiving a user utterance; generating a semantic and syntactic graph associated with the received utterance, extracting all n-grams as features from the generated semantic and syntactic graph and classifying the utterance. Classifying the utterance may be performed any number of ways such as using the extracted n-grams, a syntactic and semantic graphs or writing rules.
1. A method comprising:
receiving a user utterance as part of a natural language dialog with a human user;
applying a word n-gram classifier to the user utterance to obtain a first call type for the utterance;
when a confidence associated with the first call type meets a threshold level, associating the user utterance with the first call type to yield a classified utterance;
when the confidence associated with the first call type does not meet the threshold level, performing the steps of:
(i) generating a semantic and syntactic graph associated with the user utterance;
(ii) converting the semantic and syntactic graph into a first finite state transducer;
(iii) composing the first finite state transducer with a second finite state transducer to form a third finite state transducer, wherein the second finite state transducer comprises all possible n-grams, and wherein the third finite state transducer comprises n-grams;
(iv) extracting the n-grams as features from the third finite state transducer, to yield extracted n-grams; and
(v) associating the user utterance with a second call type based on the extracted n-grams, to yield a classified utterance, wherein the second call type is determined based on semantic and syntactic features in the extracted n-grams; and
responding to the human user in the natural language dialog based on the classified utterance.
2. The method of claim 1 , wherein the semantic and syntactic graph further comprises lexical information.
3. The method of claim 2 , wherein the semantic and syntactic graph further comprises named entities and semantic role labels.
4. The method of claim 2 , wherein the semantic and syntactic graph further comprises one of speech tags and a syntactic parse of the utterance.
5. The method of claim 1 , wherein generating the semantic and syntactic graph extends a feature set of a classifier that performs classifying the user utterance.
6. The method of claim 1 , wherein generating a semantic and syntactic graph associated with the user utterance further comprises adding transitions encoding semantic and syntactic categories of words to a word graph.
7. The method of claim 1 , wherein generating a semantic and syntactic graph associated with the user utterance further comprises utilizing one of: part of speech tags, a syntactic parse of the utterance, generic named entity tags, supertags and word stems in the graph.
8. The method of claim 1 , wherein extracting the n-grams as features from the semantic and syntactic graph further comprises enumerating all paths of the third finite state transducer.
9. The method of claim 1 , wherein classifying the user utterance further comprises classifying the user utterance using one of: the extracted n-grams, the semantic, and syntactic graph and writing rules.
10. A system comprising:
a processor; and
a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising:
receiving a user utterance as part of a natural language dialog with a human user;
applying a word n-gram classifier to the user utterance to obtain a first call type for the utterance;
when a confidence associated with the first call type meets a threshold level, associating the user utterance with the first call type to yield a classified utterance;
when the confidence associated with the first call type does not meet the threshold level, performing the steps of:
(i) generating a semantic and syntactic graph associated with the user utterance;
(ii) converting the semantic and syntactic graph into a first finite state transducer;
(iii) composing the first finite state transducer with a second finite state transducer to form a third finite state transducer, wherein the second finite state transducer comprises all possible n-grams, and wherein the third finite state transducer comprises n-grams;
(iv) extracting the n-grams as features from the third finite state transducer, to yield extracted n-grams; and
(v) associating the user utterance with a second call type based on the extracted n-grams, to yield a classified utterance, wherein the second call type is determined based on semantic and syntactic features in the extracted n-grams; and
responding to the human user in the natural language dialog based on the classified utterance.
11. The system of claim 10 , wherein the semantic and syntactic graph further comprises lexical information.
12. The system of claim 11 , wherein the semantic and syntactic graph further comprises named entities and semantic role labels.
13. The system of claim 11 , wherein the semantic and syntactic graph further comprises one of speech tags and a syntactic parse of the utterance.
14. The system of claim 10 , wherein generating the semantic and syntactic graph further extends a feature set of a classifier that classifies the user utterance.
15. The system of claim 10 , wherein generating the semantic and syntactic graph associated with the received utterance further adds transitions encoding semantic and syntactic categories of words to a word graph.
16. The system of claim 10 , wherein generating the semantic and syntactic graph associated with the user utterance further utilizes one of: part of speech tags, a syntactic parse of the utterance, generic named entity tags, supertags, and word stems in the graph.
17. The system of claim 10 , wherein extracting the n-grams as features from the semantic and syntactic graph further enumerates all paths of the third finite state transducer.
18. The system of claim 10 , wherein classifying the user utterance further classifies the user utterance using one of: the extracted n-grams, the semantic and syntactic graph and writing rules.
19. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising:
receiving a user utterance as part of a natural language dialog with a human user;
applying a word n-gram classifier to the user utterance to obtain a first call type for the utterance;
when a confidence associated with the first call type meets a threshold level, associating the user utterance with the first call type to yield a classified utterance;
when the confidence associated with the first call type does not meet the threshold level, performing the steps of:
(i) generating a semantic and syntactic graph associated with the user utterance;
(ii) converting the semantic and syntactic graph into a first finite state transducer;
(iii) composing the first finite state transducer with a second finite state transducer to form a third finite state transducer, wherein the second finite state transducer comprises all possible n-grams, and wherein the third finite state transducer comprises n-grams;
(iv) extracting the n-grams as features from the third finite state transducer, to yield extracted n-grams; and
(v) associating the user utterance with a second call type based on the extracted n-grams, to yield a classified utterance, wherein the second call type is determined based on semantic and syntactic features in the extracted n-grams; and
responding to the human user in the natural language dialog based on the classified utterance.
20. The computer-readable storage device of claim 19 , wherein the semantic and syntactic graph further comprises lexical information.
21. The computer-readable storage device of claim 20 , wherein the semantic and syntactic graph further comprises named entities and semantic role labels.
22. The computer-readable storage device of claim 20 , wherein the semantic and syntactic graph further comprises one of speech tags and a syntactic parse of the utterance.
23. The computer-readable storage device of claim 19 , wherein generating a semantic and syntactic graph extends a feature set of a classifier that performs classifying the user utterance.
24. The computer-readable storage device of claim 19 , wherein generating a semantic and syntactic graph associated with the user utterance further comprises adding transitions encoding semantic and syntactic categories of words to a word graph.
25. The computer-readable storage device of claim 19 , wherein generating a semantic and syntactic graph associated with the user utterance further comprises utilizing one of: part of speech tags, a syntactic parse of the utterance, generic named entity tags, supertags, and word stems in the graph.
26. The computer-readable storage device of claim 19 , wherein extracting n-grams as features from the semantic and syntactic graph further comprises enumerating all paths of the third finite state transducer.
27. The computer-readable storage device of claim 19 , wherein classifying the user utterance further comprises classifying the user utterance based on one of: the extracted n-grams, the semantic and syntactic graph and writing rules.