NLP System Parsing Compound Phrases via Segmentation
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Solution Overview
Problem
Natural language processing systems are limited to handling simple phrases and struggle to determine meaning for compound phrases, making it difficult to access complex database queries or issue system commands via natural language phrases.
Innovation Solution
A system that parses compound phrases into tokens using separators, identifies entities and keywords, and uses a model to determine intent, allowing for the association of entities with keywords and the construction of database queries from complex natural language inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If natural language processing systems are designed to handle simple phrases, then the system design is straightforward and reliable, but the system cannot determine meaning for compound phrases or access complex database queries
Solution Approach 1:
The system segments compound phrases into multiple simple phrases using separators (conjunctions, prepositions, punctuation). Each segmented phrase is processed independently to identify entities and keywords, then results are combined to determine the overall intent. This allows the system to handle complex queries while maintaining the reliability of simple phrase processing.
Solution Approach 2:
The system introduces an intermediary processing layer that bridges simple phrase handling and compound phrase understanding. This layer segments compound phrases, processes each segment through the existing simple phrase parser, and then aggregates results to determine intent for the complete compound phrase, enabling complex query handling without redesigning the core parser.
2Measurement precision
If the system parses compound phrases into tokens and identifies entities and keywords, then intent detection accuracy improves, but the processing complexity and computational resources increase
Solution Approach 1:
The parsing process is segmented into distinct stages: tokenization using separators, entity identification, keyword extraction, and intent determination. Each stage processes specific aspects of the input independently, improving accuracy through systematic analysis while managing complexity by breaking down the overall task into manageable sub-tasks that can be executed sequentially.
Data Source
AI summary
The system for responding to a set of words includes an interface and a processor. The interface is configured to receive the set of words. The processor is configured to determine a token from the set of words; determine an entity and an associated keyword from the token, wherein the entity is associated with an entity type; determine an intent based at least in part on the set of words; match the entity to an entity of the intent based on the associated keyword; and determine a response based on the intent and the entity.