Hybrid Grammatical and Ungrammatical Parsing for Search Engines
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Solution Overview
Problem
Current natural language processing (NLP) solutions face challenges in handling both grammatically coherent and ungrammatical inputs, as existing parsers either fail to process complex structures or are too lenient, leading to exclusive and inefficient domain-specific implementations that require large data science teams and extensive training data.
Innovation Solution
A hybrid parsing method that combines strict and loose parsing using a common model, where initial parses with open slots are filled through type-based slot filling, selecting entities based on usage frequency or proximity, to produce a final parse suitable for search engines, addressing the limitations of existing parsers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a strict parser is used to handle complex grammatical structures, then parsing accuracy for coherent sentences is improved, but the system cannot handle ungrammatical and out-of-model utterances
Solution Approach 1:
The system dynamically switches between strict parsing mode (for grammatically correct inputs) and loose parsing mode (for ungrammatical or out-of-model inputs). The parser adapts its behavior based on the input characteristics, allowing it to maintain high accuracy for coherent sentences while also handling diverse ungrammatical utterances effectively.
Solution Approach 2:
The system changes the parsing parameters by adjusting the strictness level based on the input. When processing ungrammatical or out-of-vocabulary inputs, the parser relaxes its constraints and applies looser matching rules, enabling it to handle a broader range of inputs without sacrificing accuracy for well-formed sentences.
2Adaptability or versatility
If a loose parser is used to handle various sentences, then versatility is improved, but the system cannot handle complex grammatical structures
Solution Approach 1:
The system dynamically adjusts its parsing strictness based on the input characteristics. For simple sentences, it uses loose parsing to maintain versatility, while for complex grammatical structures, it switches to strict parsing mode to ensure accurate interpretation of nested clauses and complex syntax.
Solution Approach 2:
The parser segments the input sentence into hierarchical components and applies different parsing strategies to different levels of the syntax tree. Complex grammatical structures are parsed with stricter rules at appropriate hierarchical levels, while maintaining overall versatility through the modular architecture.
3Adaptability or versatility
If machine learning techniques are used for natural language processing, then handling of natural language is improved, but large amounts of training data and data science teams are required
Solution Approach 1:
The system uses self-service through predefined grammatical models and rules that automatically handle natural language processing without requiring external machine learning training. The parser serves itself by using built-in linguistic knowledge and rules to interpret queries, eliminating the need for large data science teams and extensive training corpora.
Solution Approach 2:
The patent introduces an intermediary layer of predefined grammatical models and linguistic rules that mediate between the input natural language and the search system. This intermediary layer handles the complexity of natural language interpretation using structured models rather than requiring machine learning approaches.
Data Source
AI summary
A system and method for processing a natural language input. The natural language input is initially processed through a strict parser, which results in an initial parse. The initial parse is evaluated to determine if it exceeds a completeness threshold. If the initial parse does not pass the completeness threshold, any open slots are filled using type based slot filling. The final parse is then output for further processing by another system, such as a search engine.