Graph-Based Token Alignment for Unsupervised Paraphrase Mining
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Solution Overview
Problem
Natural language processing systems struggle to adapt to domain-specific information with limited data, necessitating techniques that can efficiently identify and utilize domain-specific phrases without extensive customization.
Innovation Solution
A graph-based unsupervised paraphrase mining system that aligns and merges tokens across sentences, determining compatibility, injectivity, monotonicity, and transitivity to generate combined graphs and identify interchangeable phrases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional NLP systems are used with limited domain-specific data, then the system can operate with simple architecture, but the system fails to adapt to domain-specific information and achieves poor performance
Solution Approach 1:
The patent segments sentences into individual tokens and represents each token as a separate node in a graph structure. This segmentation allows the system to analyze and align domain-specific terminology at the token level, enabling effective adaptation even with limited data by capturing precise semantic relationships between individual words rather than treating sentences as monolithic units.
Solution Approach 2:
The patent transforms the traditional sequence-based sentence representation into a graph-based multi-dimensional structure where tokens are nodes and alignments are edges. This dimensional transformation from 1D sequences to 2D/3D graphs enables the system to capture complex semantic relationships and paraphrases in domain-specific language, improving adaptability without requiring extensive training data.
2Measurement precision
If graph-based token alignment is performed across multiple sentences, then paraphrase identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent merges multiple sentence graphs into a combined graph structure where matching tokens across different sentences are identified and aligned. By combining information from multiple sentences into a unified graph representation, the system achieves more accurate paraphrase identification through cross-sentence token alignment while managing computational complexity through systematic merging operations.
Solution Approach 2:
The patent introduces an intermediary alignment process that matches tokens between sentences before constructing the final combined graph. This intermediary step of token alignment acts as a mediator that reduces the complexity of directly comparing all possible token combinations, enabling precise paraphrase identification by first establishing correspondence relationships between tokens across sentences.
3Manufacturing precision
If compatibility determination is performed among sentence pairs, then quality control of aligned sentences is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary compatibility determination on sentence pairs before fully processing them into the combined graph structure. By evaluating compatibility metrics in advance, the system filters out incompatible sentence pairs early in the process, ensuring high quality alignment while reducing overall processing time by avoiding exhaustive analysis of all possible sentence combinations.
Data Source
AI summary
Disclosed embodiments relate to aligning pairs of sentences. Techniques can include receiving a plurality of sentences; generating a graph for each of at least two sentences of the plurality of sentences, wherein generating a graph for each sentence of the at least two sentences comprises: identifying one or more tokens for the sentence; and connecting via edges the one or more tokens; generating a combined graph for the at least two sentences wherein generating a combined graph comprises: aligning the identified tokens of the at least two sentences of the plurality of sentences; identifying matching and non-matching tokens between the at least two sentences based on the alignment; and merging matching tokens into a combined graph node.