Holistic Knowledge Representation and Structure Learning
This paper addresses the challenge of holistic knowledge representation and structural learning through the concept of structured representation in one-dimensional spaces. Unlike traditional natural language processing (NLP) techniques, which focus on distributed representations and partial consideration of semantics, the proposed approach enables comprehensive text analysis by integrating hierarchical data organization.
The proposed method involves creating dynamic structures for representing textual corpora, where each level of the structure is based on unique objects that represent elementary and complex units, such as words, phrases, sentences, and paragraphs. By employing algorithms for object recognition, creation, and reorganization, the method facilitates efficient management of large volumes of textual data.
The results demonstrate that the proposed model overcomes the limitations of existing methods, such as scalability and precision in text analysis, while maintaining flexibility in modeling new information. The conclusions highlight the significant potential of structured representation in tasks such as text classification, sentiment analysis, and language generation.