Neural Network Semantic Vector Mapping for Document Sequence Analysis
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Solution Overview
Problem
Natural language processing systems face challenges in understanding phrases and semantic meanings due to generational nuances and the lack of capability in existing technologies, leading to poor textual recognition and consumer frustration.
Innovation Solution
A system and method for natural language processing that trains a neural network using a corpus of documents to identify significant terms, generate vectors representing semantic relationships, and map terms to document sequences, enabling the generation of timelines based on sequential order and mapped terms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional natural language processing methods are used, then the system is simple and easy to implement, but the textual recognition accuracy is poor and cannot understand semantic meanings
Solution Approach 1:
The patent replaces traditional rule-based and statistical NLP methods with a neural network-based semantic analysis system. The neural network learns semantic relationships automatically from data, substituting manual feature engineering and rigid rule systems with adaptive, data-driven models that capture nuanced meanings and contextual relationships in text.
Solution Approach 2:
The patent transforms discrete textual data into continuous vector representations that capture semantic meaning. By changing the parameter representation from categorical text tokens to continuous semantic vectors, the system enables mathematical operations on text data and captures subtle semantic differences that traditional discrete methods cannot detect.
2Adaptability or versatility
If existing natural language processing products are used, then the implementation is straightforward, but the capabilities are insufficient leading to consumer frustration
Solution Approach 1:
The patent segments the NLP task into distinct functional components: text embedding generation, semantic relationship analysis, vector space mapping, and timeline extraction. Each component is handled by specialized neural network modules that process specific aspects of semantic understanding, allowing the system to tackle complex adaptability requirements through modular, manageable subsystems.
Solution Approach 2:
The patent introduces vector representations as an intermediary between raw text input and semantic analysis output. These vectors serve as a bridge that transforms discrete text data into a continuous space where semantic relationships can be mathematically manipulated, enabling the system to handle diverse and evolving language patterns with greater versatility.
3Loss of information
If semantic relationships are captured using vector representations, then the understanding of document sequences is improved, but the computational resources required increase
Solution Approach 1:
The patent performs preliminary action by pre-training the neural network on large corpora to learn semantic relationships before deploying it for specific tasks. This pre-training phase captures general semantic patterns and relationships, allowing the model to handle new text data with better understanding while requiring less computational energy during actual operation, as the heavy lifting of learning fundamental semantic concepts has already been done.
Data Source
AI summary
A system and method for natural language processing for document sequences comprises a computing device configured to train a neural network as a function of a corpus of documents, wherein training comprises receiving the corpus of documents, identifying significant terms, and tuning, as a function of the corpus of documents, the neural network to generate a plurality of vectors for each significant term of the plurality of significant terms, a vector in a vector space representing semantic relationships between the significant terms and semantic units in the corpus of documents, receive a current document sequence including a plurality of documents in a sequential order, map a plurality of mapped terms of the plurality of significant terms to the plurality of documents as a function of the neural network and the plurality of vectors, and generate a plurality of timelines as a function of the sequential order and the mapped terms.