Finite State Transducer Semantic Parsing with Domain Information
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Solution Overview
Problem
Conventional semantic parsers are inefficient in disambiguating natural language inputs and have sub-optimal matching capabilities, leading to inaccurate meaning representations and poor user intent recognition, especially in complex information domains.
Innovation Solution
Integrating domain information into Finite State Transducers (FSTs) for natural language processing, using semantic grammars to structure FST paths and associate tokens with weights based on frequency, to generate more relevant meaning representations aligned with user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional semantic parsers are used, then natural language processing can be performed, but disambiguation efficiency and matching capability are insufficient
Solution Approach 1:
The patent segments the natural language processing task into distinct phases: tokenization, semantic role labeling, and disambiguation. By dividing the complex parsing process into manageable components, the system can apply specialized techniques to each segment, improving both accuracy and efficiency.
Solution Approach 2:
The patent introduces semantic role labels as an intermediary representation between raw natural language input and final meaning representation. This intermediate layer captures semantic information in a structured format, facilitating more accurate disambiguation and matching while maintaining processing efficiency.
2Measurement precision
If conventional semantic parsers are used, then processing can be performed, but meaning representation accuracy is poor
Solution Approach 1:
The patent enables the semantic parser to automatically learn and adapt semantic relationships from training data without requiring manual configuration of complex rules. The system self-adjusts its disambiguation strategies and matching capabilities through machine learning, improving accuracy while keeping the parser architecture relatively simple.
3Adaptability or versatility
If conventional information retrieval systems are used, then search can be performed, but structural and semantic information is ignored
Solution Approach 1:
The patent combines multiple types of information representations: token sequences, semantic role labels, and structural relationships. By integrating these different informational layers into a unified meaning representation, the system preserves both structural and semantic information while achieving versatile semantic understanding.
Data Source
AI summary
The invention relates to a system and method for integrating domain information into state transitions of a Finite State Transducer (“FST”) for natural language processing. A system may integrate semantic parsing and information retrieval from an information domain to generate an FST parser that represents the information domain. The FST parser may include a plurality of FST paths, at least one of which may be used to generate a meaning representation from a natural language input. As such, the system may perform domain-based semantic parsing of a natural language input, generating more robust meaning representations using domain information. The system may be applied to a wide range of natural language applications that use natural language input from a user such as, for example, natural language interfaces to computing systems, communication with robots in natural language, personalized digital assistants, question-answer query systems, and/or other natural language processing applications.