Natural Language Query Disambiguation via Contextual Machine Learning
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Solution Overview
Problem
Conventional search engines return irrelevant results for natural language queries containing ambiguous entities due to lack of contextual understanding, forcing users to sift through unrelated information.
Innovation Solution
A system that processes natural language queries by determining the meaning of ambiguous entities based on extracted language context and user contextual information, applying this to a machine learning model to select appropriate applications for tailored results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a conventional search engine processes a natural language query with an ambiguous entity, then all possible interpretations of the entity are returned, but the results include many irrelevant items that require user sorting
Solution Approach 1:
The patent introduces contextual information as an intermediary element between the ambiguous entity and its interpretations. This contextual information (user profile, query history, current application state) mediates the selection process by providing additional criteria to favor certain interpretations over others, thereby improving result relevance without requiring users to manually sort through all possibilities
Solution Approach 2:
The system changes the parameters used for result selection by incorporating multiple contextual dimensions (user interests, query context, application context) rather than relying solely on the ambiguous entity itself. This multi-parameter approach transforms the result ranking mechanism to prioritize interpretations that align with the aggregated contextual parameters, reducing irrelevant results
2Loss of information
If a conventional search engine returns all possible interpretations of an ambiguous entity, then completeness of information is achieved, but processing time and resource consumption increase
Solution Approach 1:
The patent extracts only the most relevant interpretations of the ambiguous entity based on contextual analysis, rather than returning all possible interpretations. By extracting and filtering results through the lens of contextual information, the system maintains completeness of relevant information while eliminating processing overhead for clearly irrelevant interpretations
Solution Approach 2:
The system performs partial action by selecting a subset of interpretations that are most likely to be relevant based on contextual probability, rather than exhaustively processing all possible interpretations. This partial approach achieves sufficient completeness for user needs while significantly improving processing efficiency
3Measurement precision
If contextual information and machine learning models are used to select applications for processing queries, then result accuracy and relevance improve, but system complexity increases
Solution Approach 1:
The patent implements a universal contextual information processing framework that serves multiple functions: disambiguating entities, ranking results, selecting applications, and personalizing user experience. This multi-functional approach consolidates what could be separate complex systems into a unified contextual processing layer, managing complexity through functional integration
Solution Approach 2:
The system performs preliminary action by pre-processing and storing contextual information (user profiles, query histories, application states) before query processing occurs. This preliminary preparation of contextual data structures enables faster, more accurate query interpretation without adding complexity to the core query processing logic, as the contextual framework is already in place
Data Source
AI summary
According to an embodiment of the present invention, a natural language query including an ambiguous entity is received from a user. A meaning of the ambiguous entity is determined based on an extracted language context of the natural language query. The determined meaning, extracted language context, and contextual information of the user is applied to a machine learning model to determine a plurality of computer applications from amongst multiple computer applications to process the natural language query. The determined applications are executed to produce results for the natural language query tailored to an interest of the user in accordance with the contextual information.