Semantic Model Intermediary for Text Representation Augmentation
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Solution Overview
Problem
Current technologies for processing natural language are limited in interpreting the meaning of text strings, relying on explicit lexical items and statistical models, which fail to capture implicit semantic content relevant for applications like document indexing, retrieval, and online advertising.
Innovation Solution
A semantic-model based system that uses external semantic models to enhance text representations by incorporating implicit semantic content, allowing for precise interpretation and augmentation of text strings, enabling better document processing and retrieval through logic-based representations and knowledge bases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical models and lexical items are used for text interpretation, then processing speed is maintained, but semantic accuracy and completeness deteriorate
Solution Approach 1:
The patent introduces semantic models as an intermediary layer between the text input and the interpretation output. These models contain pre-encoded world knowledge and semantic relationships that mediate the interpretation process, enabling the system to infer implicit meanings without requiring complex statistical analysis of every possible context.
Solution Approach 2:
The semantic models are pre-populated with extensive world knowledge, semantic relationships, and contextual information before the text processing begins. This preliminary preparation allows the system to quickly query and apply relevant semantic knowledge during interpretation, rather than having to derive all semantic relationships on-the-fly during processing.
2Loss of information
If explicit lexical items only are used, then processing simplicity is maintained, but implicit semantic content is lost
Solution Approach 1:
Semantic models serve as an intermediary that bridges the gap between explicit text and implicit meaning. The models contain pre-encoded knowledge about world concepts, relationships, and contexts that allow the system to infer implicit semantic content without having to perform complex analysis of every text element.
Solution Approach 2:
The system copies relevant semantic knowledge from the pre-built semantic models into the interpretation process. Rather than re-analyzing all semantic relationships from scratch for each text, the system queries and applies pre-computed semantic knowledge that has been copied into the models during their construction phase.
3Reliability
If semantic models are used to augment text representations, then interpretation accuracy is improved, but computational resources increase
Solution Approach 1:
The semantic models are constructed and populated with world knowledge in advance, before the actual text processing occurs. This preliminary action shifts the computational burden to an offline phase, allowing the online text processing to simply query and apply pre-computed semantic knowledge, thereby reducing real-time computational resource consumption.
Solution Approach 2:
The system copies only the specific semantic knowledge relevant to the current text being processed, rather than analyzing or storing all possible semantic relationships. This selective copying approach reduces the computational load during text processing while still providing accurate semantic interpretation.
Data Source
AI summary
A system and method for producing semantically-rich representations of texts to amplify and sharpen the interpretations of texts. The method relies on the fact that there is a substantial amount of semantic content associated with most text strings that is not explicit in those strings, or in the mere statistical co-occurrence of the strings with other strings, but which is nevertheless extremely relevant to the text. This additional information is used to both sharpen the representations derived directly from the text string, and also to augment the representation with content that, while not explicitly mentioned in the string, is implicit in the text and, if made explicit, can be used to support the performance of text processing applications including document indexing and retrieval, document classification, document routing, document summarization, and document tagging. These enhancements may be used to support down-stream processing, such as automated document reading and understanding, online advertising placement, electronic commerce, corporate knowledge management, and business and government intelligence applications.