Dhatu Vector Word Embedding for Semantic Disambiguation
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Solution Overview
Problem
Natural language processing (NLP) systems face challenges in accurately capturing the complex and context-dependent meanings of words due to the complexity of human communication, leading to difficulties in generating accurate word embeddings that reflect semantic and syntactic features.
Innovation Solution
The method employs a language-morphology-based lexical semantic extraction using Sanskrit morphological rules and Dhatus to represent English words as sparse and low-dimensional Dhatu vectors, leveraging the morphological semantics of Sanskrit to disambiguate meanings and improve interpretability in NLP tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained on text corpora to predict word meanings, then word embeddings can be generated, but the complexity of human communication makes it difficult to accurately capture all semantic and syntactic features
Solution Approach 1:
The patent segments the word meaning into distinct semantic features by mapping words to multiple Dhatus (semantic atoms). Each Dhatu represents a specific semantic component, allowing the system to break down complex word meanings into manageable, identifiable parts. This segmentation enables more precise capture of semantic features while reducing the complexity of processing overall meaning.
Solution Approach 2:
The patent transforms word representations from traditional dense vectors to sparse high-dimensional vectors based on Dhatu mappings. This dimensional transformation allows the system to represent word meanings in a space where semantic relationships are more explicitly captured through sparsity patterns, improving measurement precision while managing complexity through structured representation.
2Adaptability or versatility
If neural networks are trained to predict word meanings in context, then word embeddings can be learned, but most words have multiple context-dependent meanings that are difficult to capture
Solution Approach 1:
The patent segments word meanings into discrete Dhatus, where each Dhatu corresponds to a specific semantic component. This allows the system to represent multiple context-dependent meanings as separate, identifiable components rather than attempting to capture them as a monolithic meaning, thereby improving adaptability while maintaining precision through structured decomposition.
Solution Approach 2:
The patent enables dynamic selection of relevant Dhatus based on context. By mapping words to multiple possible Dhatus and selecting the appropriate ones based on contextual information, the system can adapt its representation to match the specific meaning intended in different contexts, improving both versatility and precision simultaneously.
3Ease of manufacture
If traditional neural network embeddings are used, then word representations can be generated, but interpretability of the vector representations is poor
Solution Approach 1:
The patent introduces Dhatus as intermediary semantic atoms that mediate between raw word representations and meaningful interpretations. These Dhatus serve as interpretable intermediaries that bridge the gap between computational processing and human understanding, allowing the system to maintain ease of embedding generation while significantly improving interpretability through meaningful semantic decomposition.
Data Source
AI summary
Described are methods and systems for graphically organizing words and their meanings. The words, in an input language such as English, are added as nodes to a graph. Synonyms of each input word taken from a second language, such as Sanskrit, are likewise added as nodes to the graph and connected to the corresponding input-word nodes via synonym edges. Root elements of the synonyms, semantic meanings, are also added to the graph as meaning nodes. The resulting graph depicts linguistic and semantic interrelations between input words, their second-language synonyms, and the meanings derived therefrom.