Fine-Grained Question Type Classification via Vector Embeddings
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Solution Overview
Problem
Current question type classification methods in Q/A systems rely on manual annotation and operate at a coarse granularity, lacking the ability to perform fine-grained classification effectively.
Innovation Solution
The method involves converting input questions into vector form using trained word embeddings, constructing a type similarity matrix with a predetermined ontology, and determining scores for all possible types based on this matrix, allowing for fine-grained classification without manual annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used for question type classification, then classification can be performed, but the process is time-consuming and lacks fine-grained classification capability
Solution Approach 1:
The patent replaces manual annotation (mechanical human operation) with automated text embedding and similarity computation. Questions are converted to vector representations using pre-trained embeddings, and classification is performed through automated similarity matching against a type similarity matrix, eliminating manual time consumption while enabling fine-grained classification.
Solution Approach 2:
The patent transforms the classification approach by changing from discrete manual category assignment to continuous vector space representation. By representing questions and types as vectors with semantic meaning, the system enables fine-grained classification through similarity calculations, achieving both precision and automation.
2Productivity
If coarse-grained classification is used, then processing is faster, but the ability to refine and rank answer options is insufficient
Solution Approach 1:
The patent maintains fast processing by using pre-computed text embeddings and similarity matrices, while achieving fine-grained classification through the vector space representation. The automated computation of similarity scores between question vectors and type vectors enables both speed and precision, allowing for effective refinement and ranking of answer options.
Solution Approach 2:
The patent performs preliminary action by pre-training word embeddings and pre-computing the type similarity matrix before actual classification. This preprocessing enables fast automated classification during operation while maintaining fine-grained capability, as the heavy computational work is done in advance.
3Productivity
If automated classification is implemented, then processing time is reduced, but fine-grained classification capability was previously lacking
Solution Approach 1:
The patent replaces manual classification mechanisms with automated text embedding and similarity computation. By using vector representations that capture semantic meaning, the automated system achieves both high productivity and fine-grained classification capability simultaneously.
Solution Approach 2:
The patent transitions from discrete category labels to continuous vector space representation, adding a dimensional aspect to classification. This vector space dimensionality enables fine-grained differentiation between question types while maintaining automated processing, as similar types cluster closer in the vector space.
Data Source
AI summary
A computer-implemented method according to one embodiment includes converting an input question into a vector form using trained word embeddings; constructing a type similarity matrix using a predetermined ontology; and determining a score for all possible types for the input question, based on the input question in vector form and the type similarity matrix.