Knowledge-Guided Structural Attention for RNN Semantic Tagging
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Solution Overview
Problem
Recurrent neural networks (RNNs) for natural language processing primarily consider word and sentence sequences as flat structures, ignoring inherent syntactic properties and hierarchical structures that provide rich, structured information, which limits their ability to understand and tag sequences effectively, especially for previously unencountered data.
Innovation Solution
The Knowledge-Guided Structural Attention Process (K-SAP) generates a knowledge-guided vector by parsing input phrases into hierarchical structures, encoding sub-structures into vectors, and applying attention weighting to determine their importance, which is then combined with the input vector to guide RNNs in semantic tagging, incorporating non-flat network topologies and prior knowledge for improved understanding and generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If RNNs use flat linear chain structure for processing sequences, then the model complexity is low and training is simple, but the ability to capture hierarchical syntactic properties and semantic relationships is limited
Solution Approach 1:
The patent segments the input sequence into hierarchical syntactic structures (constituency trees) that capture nested relationships. Instead of treating the sequence as a flat chain, it divides the processing into multiple hierarchical levels where each level represents different syntactic granularity, allowing the model to capture complex semantic relationships while maintaining manageable complexity through structured segmentation
Solution Approach 2:
The patent transitions from a one-dimensional flat sequence representation to a multi-dimensional hierarchical structure by incorporating syntactic tree levels. This dimensional expansion allows the model to simultaneously process information at different hierarchical levels (word level, phrase level, sentence level), enriching the semantic representation without proportionally increasing model complexity
2Reliability
If RNNs incorporate hierarchical syntactic structures, then the understanding of natural language improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary parsing to construct hierarchical syntactic structures before the main RNN processing. By pre-processing the input sequence into a structured representation with identified constituents and hierarchical relationships, the model eliminates the need for complex real-time hierarchical processing during RNN computation, reducing computational complexity while preserving semantic understanding capabilities
Solution Approach 2:
The patent introduces an intermediary syntactic structure layer between the raw input sequence and the RNN processing. This intermediary hierarchical representation acts as a mediator that organizes information in a computationally efficient manner, allowing the RNN to process structured semantic relationships without directly handling the full complexity of hierarchical parsing during the main computation
3Measurement precision
If attention weighting is applied to sub-structures, then the identification of important semantic elements improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies attention weighting selectively to different sub-structures based on their local semantic importance within the hierarchical tree. Instead of uniformly processing all nodes, the attention mechanism assigns different weights to different constituents based on their relevance to the overall semantic meaning, allowing precise identification of important elements while reducing computational focus on less critical regions, thereby optimizing processing time
Data Source
AI summary
Systems and methods for determining knowledge-guided information for a recurrent neural networks (RNN) to guide the RNN in semantic tagging of an input phrase are presented. A knowledge encoding module of a Knowledge-Guided Structural Attention Process (K-SAP) receives an input phrase and, in conjunction with additional sub-components or cooperative components generates a knowledge-guided vector that is provided with the input phrase to the RNN for linguistic semantic tagging. Generating the knowledge-guided vector comprises at least parsing the input phrase and generating a corresponding hierarchical linguistic structure comprising one or more discrete sub-structures. The sub-structures may be encoded into vectors along with attention weighting identifying those sub-structures that have greater importance in determining the semantic meaning of the input phrase.