Sentence Model for Answer Generation Using Semantic Clustering
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Solution Overview
Problem
Existing question and answer systems rely on word-level language models, which often choose incorrect or unnatural answers due to focusing on individual words rather than semantic relationships between sentences, leading to inefficiencies in capturing syntactic, discourse, and entailment relationships.
Innovation Solution
A sentence model is generated using sentence vectors that group semantically similar sentences into clusters, determining their frequency, and calculating probabilities to improve answer generation by encapsulating relationships between sentences, thereby reducing inappropriate answers and providing natural sentence order.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If word-level language models are used for answer generation, then the system can process individual words and basic phrases, but it fails to capture semantic relationships between sentences leading to incorrect or unnatural answers
Solution Approach 1:
The patent transitions from word-level analysis to sentence-level analysis by converting sentences into vector representations. This dimensional shift allows the system to capture semantic relationships between entire sentences rather than just individual words, enabling better understanding of context and meaning in answer generation.
Solution Approach 2:
The system changes the fundamental parameter of analysis from individual words to sentence vectors. By representing sentences as mathematical vectors in a high-dimensional space, the system can compute semantic similarity and capture relationships between sentences, thereby improving answer accuracy while maintaining processing capability.
2Measurement precision
If sentence vectors are grouped into clusters to capture semantic relationships, then the system can improve answer naturalness, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the corpus into multiple sentence clusters based on semantic similarity. By dividing the large corpus into smaller, semantically coherent groups, the system reduces the computational burden of processing all sentences uniformly while still capturing important semantic relationships for answer generation.
Solution Approach 2:
Instead of processing all sentences with equal depth, the system applies clustering to group sentences and then focuses computational resources on the most relevant clusters for a given query. This partial action approach improves answer naturalness without requiring exhaustive processing of the entire corpus.
3Reliability
If sentence clusters are used to model semantic relationships, then the system can reduce inappropriate responses, but the data sparsity problem persists as most sentence sequences are not observed in training
Solution Approach 1:
The patent merges multiple semantically similar sentences into unified sentence clusters. By combining sentences with similar meanings into single representative clusters, the system effectively increases the coverage of observed sentence sequences and reduces data sparsity, allowing the model to generalize better to unobserved cases.
Solution Approach 2:
The system creates representative sentence vectors that capture the essence of multiple similar sentences. These vector representations act as copies or abstractions of the original sentences, allowing the model to infer meaning from observed clusters even when specific sentence sequences were not seen during training.
Data Source
AI summary
In an approach to generating and using a sentence model for answer generation, one or more computer processors ingest a first corpus of a plurality of text sentences. One or more computer processors convert the plurality of text sentences into a plurality of sentence vectors. One or more computer processors group the plurality of sentence vectors into a plurality of sentence clusters, wherein a sentence cluster is composed of sentences that are semantically similar. One or more computer processors receive a second corpus. One or more computer processors determine, for each sentence cluster of the plurality of sentence clusters, a frequency each sentence cluster appears in the second corpus. Based on the determined frequency, one or more computer processors calculate a probability of each sentence cluster of the plurality of sentence clusters. Based on the calculated probabilities, one or more computer processors generate a first sentence model.