Multi-domain NLP Architecture for Real-time Ambiguity Handling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current natural language processing (NLP) and natural language understanding (NLU) systems on mobile devices face challenges in efficiently extracting semantic meaning from natural language inputs, particularly in handling ambiguity and evolving from prototypes to full systems that leverage big data sources, while maintaining compatibility with hybrid technologies and minimizing response latency.
Innovation Solution
A multi-domain NLP architecture utilizing multiple parallel domain pipelines that process natural language inputs with mention modules for rule-based and statistical-based NLU, interpretation generators for sentence-level interpretation candidates, and a global evidence ranker to produce a final output set, preserving ambiguity and allowing for real-time processing with minimal latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple parallel domain pipelines are used to process natural language inputs, then the system can preserve ambiguity and produce rich N-best lists, but the device complexity increases
Solution Approach 1:
The NLP system is divided into multiple parallel domain pipelines, each handling a specific domain (e.g., sports, entertainment, news). Each pipeline independently processes the input query through mention detection, semantic attachment, and interpretation generation, allowing the system to maintain multiple candidate interpretations simultaneously without overwhelming complexity in a single processing path.
Solution Approach 2:
A global evidence ranker serves as an intermediary component that receives N-best lists from multiple domain pipelines and performs late-stage disambiguation. This mediator consolidates results from parallel pipelines, ranks interpretations by evidence strength, and selects the most likely semantic meaning, thereby managing complexity by centralizing the decision-making process.
2Speed
If real-time processing is implemented with minimum response latency, then the system responds quickly to user queries, but the processing depth may be reduced
Solution Approach 1:
The system performs preliminary processing in each domain pipeline by detecting mentions and generating semantic attachments before full interpretation. This preliminary action prepares candidate interpretations in parallel, allowing the global evidence ranker to quickly evaluate and rank them without performing exhaustive analysis from scratch, thus maintaining both speed and precision.
Solution Approach 2:
The system dynamically adjusts processing depth based on domain relevance and confidence levels. High-confidence matches in individual domain pipelines can be processed more quickly, while ambiguous cases receive deeper analysis through the global evidence ranker. This dynamic approach optimizes response latency while maintaining semantic analysis precision where needed.
3Adaptability or versatility
If hybrid NLP technologies are combined (rule-based and statistical approaches), then the system can leverage multiple processing methods, but the ease of operation decreases
Solution Approach 1:
The system segments different NLP approaches (rule-based and statistical) into separate processing stages within each domain pipeline. Rule-based mention detection and semantic attachment rules operate in parallel with statistical interpretation generation, allowing each method to be independently configured and optimized without interfering with the other, thus maintaining ease of operation while achieving versatility.
Data Source
AI summary
An arrangement and corresponding method are described for multi-domain natural language processing. Multiple parallel domain pipelines are used for processing a natural language input. Each domain pipeline represents a different specific subject domain of related concepts. Each domain pipeline includes a mention module that processes the natural language input using natural language understanding (NLU) to determine a corresponding list of mentions, and an interpretation generator that receives the list of mentions and produces a rank-ordered domain output set of sentence-level interpretation candidates. A global evidence ranker receives the domain output sets from the domain pipelines and produces an overall rank-ordered final output set of sentence-level interpretations.