Hybrid Neural Network for Semantic Question Retrieval
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Solution Overview
Problem
Existing question and answer systems face difficulties in retrieving semantically equivalent questions due to variations in vocabulary and structure, making it challenging to find similar questions that may have the same answer.
Innovation Solution
A computer-implemented method and system using hybrid representations, which includes generating weighted bag of words and convolutional neural networks-based distributed vector representations of questions, trained by a neural network to compute semantic similarity between questions, allowing for the retrieval of semantically equivalent questions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text matching methods are used to search for similar questions, then the system is simple to implement, but it fails to identify semantically equivalent questions due to vocabulary and structure variations
Solution Approach 1:
The patent combines multiple representation methods (bag-of-words, TF-IDF, and word embeddings) into a hybrid model to capture both lexical overlap and semantic meaning. This composite approach enables the system to identify semantically equivalent questions while maintaining reasonable system complexity through modular integration of each representation component.
Solution Approach 2:
The system transforms the parameter representation of questions from simple word counts to weighted embeddings that capture semantic relationships. By changing the representation parameters from discrete tokens to continuous vector spaces, the system achieves higher precision in detecting semantic equivalence while managing complexity through efficient vector operations.
2Measurement precision
If hybrid representations with neural networks are used to compute semantic similarity, then the accuracy of question retrieval is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent pre-computes and stores question representations (bag-of-words, TF-IDF vectors, and word embeddings) in a database before retrieval operations. This preliminary action allows the system to quickly compute semantic similarity during query processing without performing heavy computational tasks in real-time, thereby reducing processing time while maintaining high retrieval accuracy.
Solution Approach 2:
The system segments the computation of semantic similarity into multiple independent components: bag-of-words similarity, TF-IDF similarity, and embedding-based similarity. Each component can be computed separately and then combined, allowing for optimized processing of each segment and reducing overall computational time while maintaining comprehensive semantic analysis.
3Adaptability or versatility
If multiple representation methods are combined to compute semantic similarity, then the ability to identify semantically equivalent questions is improved, but the device complexity increases
Solution Approach 1:
The patent implements a universal similarity computation framework that handles multiple representation methods (bag-of-words, TF-IDF, embeddings) through a common interface and unified similarity calculation mechanism. This multi-functional approach enables the system to adapt to different representation types without requiring separate processing pipelines for each method, thereby reducing overall system complexity while maintaining high adaptability.
Data Source
AI summary
Determining semantically equivalent text or questions using hybrid representations based on neural network learning. Weighted bag-of-words and convolutional neural networks (CNN) based distributed vector representations of questions or text may be generated to compute the semantic similarity between questions or text. Weighted bag-of-words and CNN based distributed vector representations may be jointly used to compute the semantic similarity. A pair-wise ranking loss function trains neural network. In one embodiment, the parameters of the system are trained by minimizing a pair-wise ranking loss function over a training set using stochastic gradient descent (SGD).