Graph-Text Fusion for NLP Contextual Understanding
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Solution Overview
Problem
Conventional natural language processing (NLP) approaches for tasks like textual entailment often fail to effectively utilize structural information from knowledge graphs, leading to a lack of contextual understanding and limited generalizability.
Innovation Solution
The method involves extracting subgraphs from a knowledge graph associated with text content, encoding this knowledge with the text using a graph representation, and combining it with text embeddings to classify the content, thereby situating context in a graph-based knowledge space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If embedding-based approaches are used for natural language inference, then text-based processing is simplified, but structural information from knowledge graphs is lost
Solution Approach 1:
The patent merges text-based embeddings with graph-based knowledge representations by combining embedding layers with graph convolutional network layers. The text encoder generates embeddings for premise and hypothesis, while the graph encoder processes knowledge graph subgraphs, and their outputs are concatenated and fed to a classifier, thus combining both text-based and structure-based information spaces.
Solution Approach 2:
The model creates a composite representation by integrating two different knowledge spaces: the text-based embedding space and the graph-based knowledge space. This is achieved by concatenating the output embeddings from the text encoder and graph encoder, forming a unified feature vector that contains both textual and structural information for classification.
2Reliability
If attention-based neural approaches are used, then contextual understanding is improved, but the function representation of attention is not explicitly externalized
Solution Approach 1:
The patent introduces graph convolutional networks as an intermediary mechanism that explicitly processes and represents relational information from knowledge graphs. Instead of relying solely on implicit attention mechanisms, the GCN layers provide an explicit computational framework for capturing structural dependencies and externalizing the representation of relational reasoning in the NLP task.
3Measurement precision
If graph-based approaches are used with narrow domain specificity, then domain accuracy is improved, but generalizability to other NLP tasks is limited
Solution Approach 1:
The patent designs a universal framework that can be applied to multiple NLP tasks including textual entailment, question answering, and other tasks requiring external knowledge. The model takes as input a premise, hypothesis, and knowledge graph, and outputs a classification that can be adapted to different task types. The architecture uses task-specific classification layers on top of the unified text-graph embedding, allowing the same core model to serve multiple purposes.
Data Source
AI summary
One embodiment of the invention provides a method for natural language processing (NLP). The method comprises extracting knowledge outside of text content of a NLP instance by extracting a set of subgraphs from a knowledge graph associated with the text content. The set of subgraphs comprises the knowledge. The method further comprises encoding the knowledge with the text content into a fixed size graph representation by filtering and encoding the set of subgraphs. The method further comprises applying a text embedding algorithm to the text content to generate a fixed size text representation, and classifying the text content based on the fixed size graph representation and the fixed size text representation.