Implicit Table Parsing for Context-Aware Semantic Search
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Solution Overview
Problem
Existing information retrieval systems face challenges in preserving contextual semantics and domain-specific language in natural language processing, leading to inefficiencies in semantic searching and response.
Innovation Solution
A method involving parsing documents to identify domain-specific terminology and action-trigger phrases using BERT and SBERT neural networks, followed by cosine similarity scoring to enhance search accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engines use graph-based algorithms to sift through webpages, then they can return relevant responses, but they fail to preserve contextual semantics and domain-specific language
Solution Approach 1:
The patent transforms the search approach by changing from keyword-based matching to semantic vector representation. Documents and queries are converted into high-dimensional vectors that capture contextual meaning, allowing the system to preserve and search based on semantic relationships rather than literal text matching.
Solution Approach 2:
The patent replaces traditional mechanical text-matching algorithms with neural network-based semantic embedding. Instead of using graph-based algorithms to sift through text, the system uses trained neural networks (BERT, SBERT) to generate semantic representations, substituting the mechanical search process with an intelligent semantic understanding approach.
2Measurement precision
If neural networks are used to capture contextual semantics, then search accuracy improves, but computational overhead increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing semantic embeddings for documents during an offline phase. When a query is received, the system only needs to compute the query vector and perform efficient similarity comparisons against pre-processed document vectors, significantly reducing online computational overhead while maintaining high search accuracy.
Solution Approach 2:
The patent segments the search process into distinct phases: offline document processing (embedding generation and storage) and online query processing (vector computation and similarity search). This segmentation allows computationally intensive operations to be performed beforehand, reducing the computational burden during actual search operations.
Data Source
AI summary
A method of parsing implicit tables within a document includes parsing, by a processor, a document to determine a pattern for a set of text that sets apart the information contained therein from the rest of the text; determining, by the processor, from the pattern of spacing, a first descriptive phrase and a second descriptive phrase; and assigning, by the processor, the first descriptive phrase as an action and the second descriptive phrase as a trigger.