Semantic Matching via Sentence Fragment Convolution
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Solution Overview
Problem
Existing semantic matching technologies ignore the impact of partial sentence fragments on the final matching result, leading to inaccurate semantic matching between sentences, as they represent entire sentences with a single vector and do not account for word order or partial matching information.
Innovation Solution
The method involves dividing sentences into fragments based on word vectors, performing a convolution operation on these fragments to create a three-dimensional tensor that maintains word order, and then reducing the tensor through integration and screening operations to obtain a target vector for determining the semantic matching degree.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If sentences are represented by a single integral vector, then the computation is simple, but the semantic matching accuracy deteriorates because partial matching information is lost
Solution Approach 1:
The patent segments sentences into multiple sentence fragments instead of treating them as a single integral unit. Each sentence is divided into overlapping fragments of fixed length, allowing partial matching information to be preserved. This segmentation enables the system to capture local semantic relationships while maintaining computational feasibility through structured processing of the fragments.
Solution Approach 2:
The patent transforms the one-dimensional sentence representation into a two-dimensional matrix structure where rows represent sentence fragments from the first sentence and columns represent sentence fragments from the second sentence. This dimensional transformation allows the system to preserve partial matching information while enabling efficient computation through matrix operations.
2Loss of information
If sentences are divided into word vectors for detailed analysis, then partial matching information is preserved, but the loss of information increases due to ignoring word order
Solution Approach 1:
The patent segments sentences into sentence fragments that maintain the original word order within each fragment. By preserving the sequential structure of words within fragments and using overlapping fragments, the system captures both partial matching information and word order relationships, avoiding the information loss associated with treating words as independent vectors.
Solution Approach 2:
The patent performs preliminary segmentation of sentences into ordered sentence fragments before conducting semantic matching. This preliminary structuring ensures that word order information is preserved in the fragment representations, allowing subsequent matching operations to utilize both partial matching information and sequential relationships.
3Productivity
If traditional integral matching is used, then the processing speed is fast, but the matching result accuracy deteriorates
Solution Approach 1:
The patent segments sentences into fixed-length sentence fragments with overlapping windows, enabling efficient processing through structured fragment comparison. This segmentation approach maintains processing speed by using fixed fragment lengths and systematic overlapping, while simultaneously improving accuracy by preserving partial matching information that would be lost in integral matching.
Solution Approach 2:
The patent transforms sentence matching into a two-dimensional fragment matrix problem, enabling efficient computation through matrix operations. This dimensional transformation allows the system to process multiple fragment comparisons in parallel while maintaining accuracy by capturing local semantic relationships in the matrix structure.
Data Source
AI summary
A method and an apparatus for determining a semantic matching degree, where the method includes acquiring a first sentence and a second sentence, dividing the first sentence and the second sentence into x and y sentence fragments, respectively, performing a convolution operation on word vectors in each sentence fragment of the first sentence and word vectors in each sentence fragment of the second sentence to obtain a three-dimensional tensor, performing integration or screening on adjacent vectors in the one-dimensional vectors of x rows and y columns, until the three-dimensional tensor is combined into a one-dimensional target vector, and determining a semantic matching degree between the first sentence and the second sentence according to the target vector.