Natural Language Query Classification via Segmentation and Feedback
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Solution Overview
Problem
Natural language processing systems face challenges in accurately classifying speech inputs in a speech-based user interface, as they often encounter ambiguous commands that require clarification, leading to difficulties in determining whether subsequent inputs relate to the same or different commands.
Innovation Solution
A method and system for processing natural language user queries that classify individual queries based on their types, using techniques such as keyword analysis, support vector machines, and neural networks to determine if queries are functional, entity-related, or clarification queries, and then processing them to define specific commands by extracting entities using statistical modeling and genetic algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language processing is used to interpret speech inputs, then the system can understand and act upon commands in natural language, but speech inputs may be ambiguous and require clarification
Solution Approach 1:
The speech input processing is segmented into multiple classification stages: first determining if the input is a functional query, entity query, or clarification query; then further classifying functional queries into specific command types. This multi-level segmentation allows the system to handle ambiguity by processing different query types through appropriate pathways, improving overall interpretation accuracy while maintaining natural language understanding capabilities
Solution Approach 2:
The system implements feedback mechanisms by identifying clarification queries and triggering clarification questions to users. When ambiguity is detected in the initial speech input, the system seeks additional information through structured feedback loops, resolving the contradiction between maintaining ease of natural language operation and ensuring reliable command interpretation
2Measurement precision
If sequential speech inputs are processed to determine if they relate to the same command or different commands, then command accuracy improves, but processing complexity increases
Solution Approach 1:
The processing system is segmented into distinct classification modules: a first classifier that determines query type (functional, entity, or clarification), and a second classifier that processes functional queries into specific command categories. This segmentation of processing complexity allows accurate sequential speech input classification while organizing system complexity into manageable, specialized components
Solution Approach 2:
The system performs preliminary classification of query types before detailed command processing. By first determining whether an input is a functional query, entity query, or clarification query, the system prepares the processing pipeline in advance, reducing overall processing complexity while maintaining high classification accuracy for sequential inputs
3Measurement precision
If multiple natural language processing techniques are combined to classify queries, then classification accuracy improves, but computational requirements increase
Solution Approach 1:
The computational workload is segmented across multiple specialized processing techniques including support vector machines, naive Bayes classifiers, and neural networks, each handling specific aspects of query classification. This segmentation allows the system to achieve high classification accuracy through complementary techniques while optimizing energy consumption by distributing computational tasks across different algorithmic approaches rather than using a single resource-intensive method
Data Source
AI summary
A method and system are provided for processing natural language user queries for commanding a user interface to perform functions. Individual user queries are classified in accordance with the types of functions and a plurality of user queries may be related to define a particular command. To assist with classification, a query type for each user query is determined where the query type is one of a functional query requesting a particular new command to perform a particular type of function, an entity query relating to an entity associated with the particular new command having the particular type of function and a clarification query responding to a clarification question posed to clarify a prior user query having the particular type of function. Functional queries may be processed using a plurality of natural language processing techniques and scores from each technique combined to determine which type of function is commanded.