Semantic Vector Representation via AMR Graph Steiner Trees
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Solution Overview
Problem
Current state-of-the-art solutions for representing relations between entities in natural language processing rely on deep neural networks that struggle with accurately capturing true semantic relations, especially when dealing with multiple pairs of entities, and ad-hoc methods like token embeddings do not guarantee semantic accuracy.
Innovation Solution
A method that generates a semantic vector representation of relations by parsing text segments into abstract meaning representation (AMR) graphs, extracting minimum Steiner trees, and using a trained graph neural network (GNN) to determine vector embeddings, which are then aggregated to represent the relation between entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep neural networks are used to represent relations between entities, then the system can process complex relations, but the accuracy of capturing true semantic relations deteriorates
Solution Approach 1:
The patent segments the relation representation task into multiple components: extracting paths between entity pairs, generating relation sentences for each path, and creating vector representations for each path separately. This segmentation allows the system to handle complex relations while maintaining accuracy by treating each path as an independent semantic unit that can be accurately represented and then aggregated.
Solution Approach 2:
The patent introduces an intermediary structure (the path-based representation system) between the input entities and the final relation representation. This intermediary involves extracting intermediate paths, generating relation sentences, and creating path vectors that mediate the transformation from raw entity pairs to accurate semantic relation representations, improving both handling of complexity and accuracy.
2Ease of manufacture
If ad-hoc methods like token embeddings are used to represent relations, then the implementation is simple, but the semantic accuracy deteriorates
Solution Approach 1:
The patent segments the relation representation into multiple path components between entity pairs, where each path is independently extracted and represented. This segmentation maintains implementation feasibility while improving semantic accuracy by capturing the nuanced relationships through multiple intermediate paths rather than relying on simple token embeddings.
Solution Approach 2:
The patent changes the representation parameters from simple token embeddings to path-based vector representations. By transforming the relation representation from direct token concatenation to aggregated path vectors with controlled granularity (number of paths, path length), the system achieves better semantic accuracy while maintaining computational feasibility through parameter control.
3Measurement precision
If multiple paths between entities are extracted to improve relation representation, then the semantic accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by extracting a controlled number of top-k paths between entity pairs rather than all possible paths. This selective extraction achieves sufficient semantic accuracy by focusing on the most relevant paths while avoiding the computational burden of processing excessive paths, thus balancing accuracy improvement with computational complexity management.
Data Source
AI summary
A computer-implemented method for automatically generating a semantic vector representation of a relation between a specific set of entities in natural language text is provided. The method may include, in response to receiving a text segment comprising a set of entities, automatically parsing the text segment into an abstract meaning representation (AMR) graph comprising nodes representing the set of entities. The method may further include extracting a number of minimum Steiner trees from the AMR graph, and wherein each Steiner tree comprises a minimum amount of edges between the nodes corresponding to a first entity and at least one second entity. The method may further include using a trained graph neural network (GNN) to determine vector embeddings for the minimum Steiner trees. The method may further include aggregating the vector embeddings to generate the semantic vector representation of the relation between the specific set of entities.