Text Sentence to Image Conversion via Semantic Role Segmentation
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Solution Overview
Problem
Existing techniques fail to automatically extract and represent the content of text sentences as images, leading to confusion when dealing with complex texts, as they either use too many or too few images, often omitting important concepts.
Innovation Solution
A method that recursively identifies semantic roles in text sentences, such as verbs and adjuncts, to select a minimal number of images from a database that accurately represent the semantic meaning, splitting sentences into fragments if necessary, to ensure each semantic role is depicted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple images are used to represent each word or phrase in a text sentence, then the representation becomes more detailed, but the number of images increases excessively and becomes confusing
Solution Approach 1:
The patent segments a text sentence into multiple sentence fragments, where each fragment contains a verb and its associated arguments (subject, object, etc.). This segmentation allows the system to process and represent semantic roles at a manageable level, avoiding the confusion of representing every individual word with separate images while still capturing the essential meaning of the sentence through structured fragments.
2Reliability
If manual image selection is used to complement text, then image quality can be controlled, but user effort and time consumption increase significantly
Solution Approach 1:
The patent implements an automatic image selection system that queries an image database using semantic role information extracted from the text sentence. The system autonomously selects images that match the semantic roles (such as actor, action, object) without requiring manual user intervention. This self-service approach maintains reliable image quality by using structured semantic queries while eliminating the time-consuming manual selection process.
3Measurement precision
If semantic role analysis is performed to improve image selection accuracy, then representation quality improves, but system complexity increases
Solution Approach 1:
The patent divides the complex task of semantic analysis into manageable sentence fragments, each focusing on a specific verb and its immediate arguments. This segmentation simplifies the overall system complexity by breaking down the semantic role analysis into smaller, more tractable units that can be processed independently and then combined to form the complete image representation.
Solution Approach 2:
The patent introduces sentence fragments as an intermediary structure between the raw text sentence and the final image selection. These fragments serve as a mediator that captures the essential semantic roles (verb, subject, object, etc.) in a structured format, making the subsequent image querying process more efficient and accurate without requiring the system to handle the full complexity of the entire sentence at once.
4Loss of information
If important concepts are included in text representation, then comprehension improves, but the number of images required increases
Solution Approach 1:
The patent extracts and represents only the essential semantic roles from text sentences - specifically the verb and its key arguments (subject, object, etc.). By taking out and focusing on these critical semantic elements rather than attempting to represent every word or minor detail, the system maintains high comprehension quality while minimizing the number of images required to convey the core meaning of the sentence.
Data Source
AI summary
A text sentence is automatically converted to an image sentence that conveys semantic roles of the text sentence. This is accomplished by identifying semantic roles associated with each verb of a sentence, any associated verb adjunctions, and identifying the grammatical dependencies between words and phrases in a sentence, in some embodiments. An image database, in which each image is tagged with descriptive information corresponding to the image depicted, is queried for images corresponding to the semantic roles of the identified verbs. Unless a single image is found to depict every semantic role, the text sentence is split into two smaller fragments. This process is the repeated and performed recursively until a number of images have been identified that describe each semantic role of each sentence fragment.