Confidence Level Determination for NLP Attribute Extraction
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Solution Overview
Problem
The ambiguity in natural language constructs, such as polysemy and nuances in word relationships, hinders the effective extraction of information from unstructured texts, making it challenging to determine confidence levels for attribute values of informational objects.
Innovation Solution
A method and system that perform syntactico-semantic analysis on natural language texts to produce semantic structures, which are then interpreted using production rules to associate attribute values with informational objects, and evaluate confidence levels using a confidence function, enhancing the accuracy of information extraction by accounting for the reliability of production rules and semantic classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If syntactico-semantic analysis and production rules are used to extract information from natural language text, then information extraction capability is improved, but reliability of extracted attribute values deteriorates due to inherent ambiguity of natural language constructs
Solution Approach 1:
The system implements feedback by evaluating confidence levels for each extracted attribute value using confidence functions associated with production rules. This feedback mechanism allows the system to assess the reliability of its own extraction process and identify cases where ambiguity may have led to incorrect extractions, enabling subsequent correction or verification steps.
Solution Approach 2:
The patent transforms the extracted information by adding a new parameter - confidence level - to each attribute value. This parameter change converts the extraction output from simple attribute-value pairs to structured data including reliability metrics, allowing downstream systems to weight or filter information based on extraction confidence.
2Reliability
If confidence levels are determined for all attribute values, then reliability of information extraction is improved, but computational complexity increases
Solution Approach 1:
The system applies partial action by determining confidence levels selectively rather than uniformly for all attribute values. Confidence evaluation can be applied only to critical attributes or cases where production rule matching is uncertain, reducing unnecessary computational overhead while maintaining reliability where it matters most.
Solution Approach 2:
The patent implements local quality by applying different levels of confidence evaluation to different parts of the extraction process. Critical attributes or ambiguous cases receive full confidence evaluation, while clear-cut cases may use simplified assessment, optimizing the balance between reliability and computational complexity across different regions of the extraction task.
Data Source
AI summary
Systems and methods for determining confidence levels associated with attribute values of informational objects. An example method comprises: receiving a natural language text; performing syntactico-semantic analysis of the natural language text to produce a plurality of semantic structures; interpreting the plurality of semantic structures using a set of production rules to produce a plurality of data items, each data item associating an attribute value with an informational object representing an entity referenced by the natural language text; and determining, for at least one data item of the plurality of data items, a confidence level associated with the attribute value, by evaluating a confidence function associated with the set of production rules.