Architecture and processes for computer learning and understanding
First Claim
1. A computer-implemented method, comprising:
- maintaining a collection of uninstantiated semantic structures in a memory of a computing system;
receiving, by the computing system, a natural language input;
performing, by the computing system, a syntactic analysis of the natural language input to produce one or more linguistic analysis results;
instantiating one or more uninstantiated semantic structures with natural language information from the one or more linguistic analysis results to form one or more instantiated semantic structures;
creating, by the computing system, a semantic structure with the one or more instantiated semantic structures to represent the natural language input based at least in part on using the one or more linguistic analysis results and knowledge induced from large language corpora, wherein the semantic structure includes multiple generative semantic primitive (GSP) structures defined with roles that indicate one or more beliefs regarding a first understanding of the natural language input, wherein defining the roles includes mapping one or more entities in the natural language information to the roles;
generating, by the computing system, one or more structured questions to evaluate the semantic structure and the one or more beliefs;
maintaining a dependency structure for linear dialogs to identify questions that are independent from one another;
submitting, from the computing system to a separate user computing device, the one or more structured questions for presentation to a human user;
receiving, by the computing system from the separate user computing device, one or more responses indicative of input from the human user when answering the one or more structured questions; and
revising, by the computing system, the semantic structure to produce a modified semantic structure that includes a new GSP structure defined with at least one new role that indicates a new belief regarding a second understanding of the natural language input based at least in part on the one or more responses received from the separate user computing device, the modified semantic structure representing the natural language input differently than how the semantic structure before revision represented the natural language input based at least in part on the second understanding of the natural language input learned from the human user.
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Abstract
An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system'"'"'s current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.
175 Citations
20 Claims
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1. A computer-implemented method, comprising:
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maintaining a collection of uninstantiated semantic structures in a memory of a computing system; receiving, by the computing system, a natural language input; performing, by the computing system, a syntactic analysis of the natural language input to produce one or more linguistic analysis results; instantiating one or more uninstantiated semantic structures with natural language information from the one or more linguistic analysis results to form one or more instantiated semantic structures; creating, by the computing system, a semantic structure with the one or more instantiated semantic structures to represent the natural language input based at least in part on using the one or more linguistic analysis results and knowledge induced from large language corpora, wherein the semantic structure includes multiple generative semantic primitive (GSP) structures defined with roles that indicate one or more beliefs regarding a first understanding of the natural language input, wherein defining the roles includes mapping one or more entities in the natural language information to the roles; generating, by the computing system, one or more structured questions to evaluate the semantic structure and the one or more beliefs; maintaining a dependency structure for linear dialogs to identify questions that are independent from one another; submitting, from the computing system to a separate user computing device, the one or more structured questions for presentation to a human user; receiving, by the computing system from the separate user computing device, one or more responses indicative of input from the human user when answering the one or more structured questions; and revising, by the computing system, the semantic structure to produce a modified semantic structure that includes a new GSP structure defined with at least one new role that indicates a new belief regarding a second understanding of the natural language input based at least in part on the one or more responses received from the separate user computing device, the modified semantic structure representing the natural language input differently than how the semantic structure before revision represented the natural language input based at least in part on the second understanding of the natural language input learned from the human user. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computer-implemented method, comprising:
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parsing, by a computing system, a natural language story to produce a syntactic representation of the natural language story; performing, by the computer system, a predicate argument structure (PAS) analysis on the syntactic representation of the natural language story; assigning, by the computer system, one or more entity types to one or more words in the natural language story; determining, by the computer system, co-reference chains in the one or more words in the natural language story; inferring, by the computing system, one or more semantic structures as a semantic representation of the natural language story using, at least in part, the syntactic representation of the natural language story, the one or more semantic structures including generative semantic primitive (GSP) structures defined with roles, wherein the inferring includes associating a semantic structure of the one or more semantic structure with a particular theme or a particular context of the natural language story; engaging in multiple dialog sessions with computing devices of multiple human users in parallel, by generating and submitting one or more structured questions for presentation via computer user interface to the computing devices of the multiple human users, to receive responses from the multiple human users for use by the computing system to evaluate the one or more semantic structures as an understanding of the natural language story; and responsive to the responses from the multiple human users, revising the one or more semantic structures by defining at least one new GSP structure with at least a new role for the semantic structure, the at least one new GSP structure having an above threshold probability of being included in the semantic structure associated with the particular theme or the particular context of the natural language story. - View Dependent Claims (8, 9, 10, 11, 12, 13)
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14. A computing system, comprising:
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a datastore containing a current world model that expresses beliefs about how natural language is understood; one or more processors; and memory coupled to the one or more processors, the memory storing computer-executable modules comprising; a story parsing engine to syntactically analyze a natural language story to produce linguistic analysis results; a knowledge induction engine to induce information from large language corpora to form induced information; a knowledge integration engine to form semantic structures that provide a semantic representation of the natural language story, the knowledge integration engine using the linguistic analysis results, information from the current world model, and the induced information to form the semantic structures, wherein the semantic structures include generative semantic primitive (GSP) structures defined with a predicate and one or more roles that indicate beliefs regarding an understanding of the natural language story, the knowledge integration engine including belief generation components to produce a probability distribution over a set of beliefs about the understanding of the natural language story; and a dialog engine to facilitate a dialog session with a human user by generating and submitting one or more questions for presentation via a computer user interface to a computing device used by the human user and collecting one or more responses from the computing device indicative of input from the human user, wherein the dialog engine maintains a dependency structure for linear dialogs to identify questions that are independent from one another; and wherein the knowledge integration engine revises the semantic structures by defining at least one new GSP structure that indicates a new belief regarding the understanding of the natural language story, the new belief having an above threshold probability of being relevant to the understanding of the natural language story based at least in part on the one or more responses from the human user, and wherein the knowledge integration engine updates the current world model in the datastore. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification