Neural Network for Natural Language Interpretation
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Solution Overview
Problem
Existing natural language interpretation technologies, such as neural networks, often result in partial or complete loss of original sentence meaning during translation, and fail to effectively combine word meanings within a sentence context, leading to suboptimal translation quality and inefficiency in reusing expert translator expertise.
Innovation Solution
A multilayer neural network is designed with specific layers for interpreting individual words and phrases, using binary features to distinguish between basic and additional meanings, and an output neuron that combines these interpretations based on word order to produce accurate translations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing neural networks are used for translation, then translation speed is improved, but translation quality deteriorates due to partial or complete loss of original sentence meaning
Solution Approach 1:
The neural network is divided into multiple layers (first layer for individual words, intermediate layers for phrases, last layer for complete sentences). Each layer processes linguistic units at different granularity levels, preventing loss of meaning by systematically building up from word-level to sentence-level interpretations.
Solution Approach 2:
The patent introduces an additional dimensional structure by organizing neural network layers according to linguistic hierarchy (word → phrase → sentence). This multi-dimensional approach allows simultaneous processing of different linguistic levels, preserving both speed and quality.
2Productivity
If existing neural networks interpret sentences, then processing efficiency is improved, but meaning preservation deteriorates due to failure to combine word meanings within sentence context
Solution Approach 1:
The network segments processing into distinct layers: first layer handles individual word meanings, intermediate layers combine them into phrase meanings, and the final layer integrates phrases into complete sentence interpretations. This segmented approach prevents information loss by ensuring each level contributes to the final meaning.
Solution Approach 2:
The patent systematically merges meanings across layers: word meanings are combined into phrase meanings in intermediate layers, which are then merged into complete sentence meanings in the final layer. This progressive combining ensures context is preserved while maintaining processing efficiency.
3Measurement precision
If expert translator expertise is not digitized, then translation quality is maintained through human judgment, but productivity deteriorates due to loss of expertise at project end
Solution Approach 1:
The system creates a digital copy of expert translator knowledge by training the neural network on professionally translated sentences. The network learns to replicate expert judgment patterns, enabling quality translation without requiring the original human experts to be continuously available, thus improving productivity while maintaining quality.
Data Source
AI summary
The invention relates to computer systems and is intended for interpreting sentences in a natural language. A neural network consists of layers of neurons. A first neuron layer is intended for interpreting the meanings of individual words in a sentence. Intermediate neuron layers are intended for interpreting phrases in a sentence, in which the number of words in a phrase corresponds to the number of the neuron layer. The last neuron layer is represented by an output neuron and is intended for interpreting a sentence on the basis of the lexical meanings interpreted in the preceding neuron layers. Each neuron of the input and intermediate layers comprises a memory cell intended for storing the meanings of a word or a phrase. Moreover, in order to improve the quality of interpretation, each such meaning is assigned a binary feature, the coding of which is described according to the rule whereby “1” is the primary meaning of a word or phrase and “0” is the secondary meaning of the word or word group.