Semantic Question Topic Clustering for Accurate Q&A Extraction
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Solution Overview
Problem
Existing automatic topic extraction models in natural language processing often fail to accurately identify semantically similar text samples or incorrectly group unrelated text samples together, leading to inefficiencies in question-and-answer sessions.
Innovation Solution
Combining textual overlap analysis with semantic similarity-based approaches to cluster documents, adjusting clusters iteratively based on semantic similarity, and using vector space models to refine cluster assignments, followed by extracting representative topics from each cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic topic extraction models are used to group text samples, then text samples can be categorized efficiently, but the models often fail to identify common text samples or incorrectly group unrelated text samples together
Solution Approach 1:
The patent segments the topic extraction process into multiple stages: initial clustering using keyword overlap, iterative refinement using semantic similarity, and final validation. This multi-stage segmentation allows the system to achieve both efficiency in initial grouping and precision in final categorization by progressively refining clusters.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously evaluates cluster quality and adjusts clustering parameters iteratively. User feedback on topic accuracy is incorporated to refine the semantic similarity thresholds and improve future clustering decisions, thereby enhancing both efficiency and precision over time.
2Speed
If traditional automatic topic extraction models are used, then processing speed is maintained, but accuracy of topic identification deteriorates
Solution Approach 1:
The patent performs preliminary clustering using fast keyword overlap methods before applying more computationally intensive semantic similarity analysis. This preliminary action quickly groups obvious candidates together, reducing the number of text samples that require detailed semantic analysis and maintaining overall processing speed while improving accuracy.
Solution Approach 2:
The patent dynamically adjusts the balance between speed and accuracy by adapting the depth of semantic analysis based on cluster confidence levels. High-confidence clusters are finalized quickly, while uncertain clusters receive more intensive processing, optimizing the trade-off between processing speed and topic identification accuracy.
Data Source
AI summary
A method, computer system, and computer program product are provided for automatically extracting semantically-similar question topics. A set of documents, wherein each document includes a plurality of words. One or more clusters of documents are identified in the set of documents based on a presence of common words in the documents of the one or more clusters. The one or more clusters are adjusted based on semantic similarity by adding or removing one or more documents from the one or more clusters. A topic is extracted from each adjusted cluster of documents.