Grammatical Tag Processing for Template Text Generation
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Solution Overview
Problem
Existing natural language generation techniques, such as template-based and linguistic-based methods, face limitations in flexibility and complexity, struggling to account for grammatical context, style, and tone variations, requiring numerous templates or complex algorithms, and often fail to generate grammatically correct text efficiently.
Innovation Solution
The use of templates with grammatical tags that indicate actor characteristics, such as subject-verb agreement and verb conjugation, along with parameters to determine proper text generation, and a post-generation analysis for correcting grammatical errors like punctuation, capitalization, and spacing, allows for flexible and efficient generation of human-readable text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If template-based natural language generation techniques are used, then text generation is simplified, but the system fails to account for grammatical context, style, and tone variations, requiring numerous templates
Solution Approach 1:
The patent applies parameter changes by introducing grammatical tags with parameters (such as subject number, person, tense, mood, voice) that dynamically control template instantiation. Instead of creating numerous static templates for different grammatical contexts, the system uses a single template that adapts its behavior based on parameter values provided by the grammatical tag processor, thereby reducing template complexity while maintaining grammatical adaptability
Solution Approach 2:
The patent implements dynamics by making the template processing system adaptive rather than static. The grammatical tag processor dynamically determines appropriate parameter values based on the semantic structure and grammatical context, allowing the same template to generate grammatically correct variations for different contexts (e.g., singular/plural subjects, different tenses, active/passive voice) without requiring separate templates for each case
2Reliability
If linguistic-based natural language generation methods are used, then grammatical context can be accounted for, but the algorithms become complex and require numerous templates
Solution Approach 1:
The patent applies segmentation by dividing the natural language generation process into distinct functional components: a template processor that handles text generation, a grammatical tag processor that analyzes semantic structure and determines grammatical parameters, and a template instantiation mechanism that combines them. This segmentation allows each component to specialize in one aspect, reducing overall algorithmic complexity while maintaining grammatical accuracy through coordinated operation of the parts
Solution Approach 2:
The patent introduces an intermediary mechanism in the form of grammatical tags that serve as a bridge between the semantic content and the grammatical structure. The grammatical tag processor acts as an intermediary that translates semantic information into grammatical parameters, which then guide template instantiation. This intermediary approach simplifies the overall algorithm by providing a clear interface between semantic analysis and grammatical generation
3Reliability
If numerous templates are used to account for grammatical variations, then grammatical accuracy improves, but the system complexity and maintenance burden increase
Solution Approach 1:
The patent applies universality by designing a single template structure that can serve multiple grammatical functions through parameter-based instantiation. Instead of maintaining separate templates for different grammatical contexts (singular/plural, past/present, active/passive), the system uses one universal template that adapts its output based on parameter values from the grammatical tag processor, dramatically reducing the number of templates to maintain while preserving grammatical correctness
Data Source
AI summary
Method and apparatus for automatically generating text in a human language using a template. The template may include at least one grammatical tag that implicates at least one actor in a sentence in the template. Human-language text may be determined to fill in the tag based on a characteristic of the actor, such as, for example, the actor's gender, whether the actor is singular or plural, or some other characteristic of the actor.