Missing Semantics Complementing Method for Natural Language Processing
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Solution Overview
Problem
Natural language understanding systems face challenges in recognizing specific meanings of human language due to coreference and ellipsis, leading to loss of syntax or semantics, which affects the accuracy and efficiency of human-machine dialog systems.
Innovation Solution
A method and apparatus for missing semantics complementing that involves obtaining historical dialog statements and question statements, resolving coreferences, detecting and completing ellipted components using sequence labeling models and CNN-based models, and merging the results to form a complete and accurate question statement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If coreference and ellipsis are used in natural language dialog, then expression becomes more concise and natural, but syntax or semantics components are lost
Solution Approach 1:
The system performs preliminary actions by resolving coreferences and restoring ellipsed content before further natural language understanding processing. The coreference resolution module identifies and replaces pronouns with their antecedents, while the ellipsis restoration module infers and inserts missing content, ensuring complete semantic information is available for subsequent processing steps.
Solution Approach 2:
The patent introduces intermediary modules (coreference resolution module, ellipsis restoration module) that act as mediators between the input natural language and the understanding system. These modules preprocess the input by resolving coreferences and restoring ellipses, serving as intermediaries that transform incomplete expressions into complete ones without altering the original user intent.
2Measurement precision
If coreference resolution and ellipsis restoration are performed, then missing semantics are complemented, but processing complexity increases
Solution Approach 1:
The patent segments the complex task of missing semantics complementation into distinct independent modules: a coreference resolution module for handling pronouns, an ellipsis restoration module for inferring omitted content, and a merging module for integrating results. This segmentation allows each module to specialize in specific tasks, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The system performs coreference resolution and ellipsis restoration as preliminary actions before main natural language understanding processing. By completing these tasks in advance through dedicated modules, the system prepares complete and accurate question statements for subsequent processing, improving measurement precision without burdening the main processing pipeline with complex analysis.
3Measurement precision
If multiple processing steps are applied to resolve coreferences and restore ellipses, then accuracy of complemented question statement improves, but processing time increases
Solution Approach 1:
The patent implements coreference resolution and ellipsis restoration as preliminary actions that complete the semantic analysis before the main question answering process. By performing these time-consuming tasks in advance through optimized modules, the system ensures high accuracy in the final question statement while separating the time cost from the main processing timeline, allowing subsequent steps to operate on pre-resolved data.
Solution Approach 2:
The system creates copies of the input question statement through multiple processing stages, generating resolved versions with coreferences expanded and ellipses restored. These copies are then merged and evaluated to produce the final accurate question statement. The copying process allows parallel processing and evaluation of multiple versions, improving accuracy while managing time through iterative refinement rather than single-pass analysis.
Data Source
AI summary
A missing semantics complementing method in the field of natural language processing in the artificial intelligence field is provided. The method includes: obtaining a question statement and a historical dialog statement; resolving a to-be-resolved item in the question statement based on the historical dialog statement and location information of the to-be-resolved item, to obtain a resolved question statement; determining whether a component in the question statement is ellipted, and if a component in the question statement is ellipted, complementing the ellipted component based on the historical dialog statement, to obtain a question statement after ellipsis resolution; merging the resolved question statement and the question statement after ellipsis resolution, to obtain a merged question statement; and determining a target complemented question statement from the resolved question statement, the question statement after ellipsis resolution, and the merged question statement.