Vector Generation Using Definition Sentence Context Encoding
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Solution Overview
Problem
Existing methods for vectorizing words in sentence pairs using definition sentences from a dictionary database lose significant information and do not accurately represent the relationship between the input sentence and the definition sentence, leading to suboptimal vector generation for natural language processing tasks.
Innovation Solution
A vector generating device and method that incorporates a definition-sentence-considered-context encode unit to generate vectors for input sentences by utilizing definition sentences from a dictionary database, ensuring that the relationship between the input sentence and the definition sentence is preserved and used to create accurate vector representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If definition sentences from dictionary database are used to vectorize words in sentence pairs, then coverage of low-frequency words and technical terms is improved, but significant information is lost and accuracy of vector representation deteriorates
Solution Approach 1:
The patent segments the vector generation process into multiple components: (1) generating initial word vectors from definition sentences using a neural network, (2) generating context vectors from the input sentence pair, and (3) combining these vectors through attention mechanisms. This segmentation allows each component to specialize - the definition sentence vectors capture word meaning while context vectors capture sentence-specific information, resolving the contradiction between coverage and accuracy.
Solution Approach 2:
The patent merges multiple information sources - definition sentences from the dictionary database and context from the input sentence pair - into a unified vector representation. By combining these complementary sources through attention-based fusion, the system achieves both broad coverage of vocabulary and high accuracy in representing the specific sentence context.
2Adaptability or versatility
If definition sentences are converted into word vectors using existing methods, then vocabulary coverage is expanded, but the relationship between input sentence and definition sentence is not preserved accurately
Solution Approach 1:
The patent implements feedback mechanisms where the context encode unit processes the input sentence pair and generates context vectors that are fed back into the vector generation process. This feedback allows the system to adjust the weighting and selection of definition sentence information based on the actual sentence context, preserving relationship accuracy while maintaining vocabulary coverage.
Solution Approach 2:
The system dynamically adjusts the contribution of definition sentences versus context information based on the specific input sentence pair. The attention mechanisms and context encoding allow the system to adaptively weight different information sources, ensuring that the relationship between input sentence and definition sentence is preserved accurately for each specific case while maintaining broad vocabulary coverage.
Data Source
AI summary
To make it possible to accurately generate a word vector even if vocabulary of a word vector data set is not limited.In a vector generating device 10 that generates vectors representing an input sentence P, when generating a series of the vectors representing the input sentence P based on vectors corresponding to words included in the input sentence P, a definition-sentence-considered-context encode unit 280 generates, based on a dictionary DB 230 storing sets of headwords y and definition sentences Dy, which are sentences defining the headwords y, concerning a word, which is the headword stored in the dictionary DB, among the words included in the input sentence P, the series of the vectors representing the input sentence P using the definition sentence Dy of the headwords y.