Graph-Based NLP Sentiment Extraction for Resource-Constrained Devices
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Solution Overview
Problem
Current natural language processing (NLP) systems require large amounts of data and complex matrix-based operations to extract sentiment, which is computationally expensive and limits their flexibility and adaptability, especially when encountering unseen words.
Innovation Solution
The implementation of graph-based NLP classification models that divide input statements into bigrams, identify relationships between neighboring words using a graph-based model, and determine sentiment classifications through a summation of these relationships, allowing for sentiment extraction with less data and less complex operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If matrix-based operations are used to process NLP data, then sentiment classification accuracy can be achieved, but computational resources and processing complexity increase substantially
Solution Approach 1:
The patent replaces traditional matrix-based operations with graph-based operations. Instead of using matrix multiplication and linear algebra operations to process NLP data, the system uses graph structures where nodes represent words or concepts and edges represent relationships between them. This substitution fundamentally changes the computational approach from dense matrix operations to sparse graph operations, reducing computational complexity while maintaining sentiment classification accuracy.
Solution Approach 2:
The patent changes the fundamental parameters of the NLP processing system by transitioning from vector-space models (matrix-based) to graph-based representations. This parameter change involves representing text data as graphs with nodes and edges rather than as vectors in high-dimensional space, which fundamentally alters how computations are performed and reduces the computational resources required.
2Measurement precision
If large amounts of data are used to train NLP models, then model accuracy improves, but data storage and processing requirements increase
Solution Approach 1:
The patent extracts and leverages the structural relationships and semantic information already present in the training data by representing them as graph structures. Instead of requiring large volumes of raw text data for training, the system extracts the essential relational patterns into graph representations that can be trained with smaller datasets. The graph structure captures the inherent semantics and relationships, reducing the need for extensive training data.
Solution Approach 2:
The patent performs preliminary processing to convert training data into graph structures before the actual sentiment classification task. By pre-processing the data into graph representations with nodes, edges, and relationship labels, the system prepares the data in a format that is more efficient for training and inference, reducing the computational burden during model training and deployment.
3Adaptability or versatility
If traditional NLP models are used to handle unseen words, then model flexibility is limited, but adapting to new vocabulary requires retraining
Solution Approach 1:
The patent creates a universal graph-based representation system that can handle both known and unseen words through the same mechanism. The graph structure uses relationship types and node attributes that are language-agnostic, allowing the model to process any word by its contextual relationships rather than relying on pre-trained embeddings for specific vocabulary. This universal approach enables the system to adapt to new words without retraining, as long as the words can be represented as nodes with appropriate relationship types.
4Adaptability or versatility
If resource-constrained devices are used for NLP operations, then deployment flexibility improves, but processing capability is limited
Solution Approach 1:
The patent substitutes computationally intensive matrix operations with more efficient graph-based operations that are better suited for resource-constrained devices. Graph operations can be implemented with lower computational overhead and memory requirements, enabling deployment on devices like Raspberry Pi while maintaining sentiment analysis functionality. The graph representation allows for optimized algorithms that reduce the processing power needed compared to traditional deep learning approaches.
Data Source
AI summary
System and methods for extracting sentiment from statements using graph-based classification models. In embodiments, an input statement is divided into a set of bigrams, each bigram composed of a respective pair of neighboring words in the input statement. A relationship between the neighboring words is identified for each bigram of the set of bigrams using a graph-based NLP classification model having a plurality of nodes, each node associated with a node of the graph-based NLP classification model. The relationship between two nodes includes information for determining a sentiment classification associated with the relationship between the two nodes. A sentiment classification for each bigram of the set of bigrams is determined based on the relationship between the respective neighboring words of each bigram, and an overall sentiment classification for the input statement is determined based on a summation of the sentiment classification for each bigram of the set of bigrams.