Statistical Key Phrase Extraction for Document Characterization
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Solution Overview
Problem
Current methods for characterizing documents, such as full text search and document-level semantics, require significant investments in hardware and personnel resources, and often involve replicating processes, leading to additional costs and complexity in identifying key features like key phrases.
Innovation Solution
A method that calculates the frequency of textual phrases within a document and uses a language model to compute cross-entropy values, selecting statistically significant phrases and ranking them based on occurrence and location within the document, to populate a key phrase data structure efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full text search solutions or document level semantics mining techniques are used to characterize documents, then document characterization accuracy is improved, but hardware and personnel resource requirements increase significantly
Solution Approach 1:
The patent extracts only the most relevant textual phrases from documents based on statistical significance rather than processing entire documents. This selective extraction approach maintains characterization accuracy while reducing resource consumption by focusing computational effort only on identifying key phrases rather than comprehensive document analysis
Solution Approach 2:
The patent uses inexpensive statistical methods (frequency counting and cross-entropy calculation) instead of expensive sophisticated NLP models. These simple computational approaches provide sufficient document characterization without requiring heavy hardware investments or specialized personnel, effectively replacing costly processing with affordable statistical analysis
2Adaptability or versatility
If multiple document processing techniques (search and semantics) are implemented, then comprehensive document analysis capability is improved, but operational costs increase due to process replication
Solution Approach 1:
The patent creates a universal key phrase extraction method that serves multiple document processing needs simultaneously. The same statistical analysis infrastructure supports both search functionality and semantic analysis, eliminating the need for separate processing pipelines and reducing operational costs while maintaining comprehensive document analysis capability
Solution Approach 2:
The patent merges document search and semantic analysis into a single integrated process. By combining frequency analysis and cross-entropy calculation into one unified approach, the system achieves both search relevance and semantic understanding without duplicating processing efforts, thereby reducing operational costs while maintaining versatility
3Reliability
If complex document mining techniques are used to satisfy organization's document processing needs, then processing comprehensiveness is improved, but system integration complexity and costs increase
Solution Approach 1:
The patent segments the complex document processing task into simple, manageable components: frequency calculation and cross-entropy computation. This segmentation breaks down sophisticated NLP processing into basic statistical operations that are easier to implement and integrate, reducing system integration complexity while maintaining processing comprehensiveness through systematic phrase evaluation
Data Source
AI summary
The present invention extends to methods, systems, and computer program products for identifying key phrases within documents. Embodiments of the invention include using a tag index to determine what a document primarily relates to. For example, an integrated data flow and extract-transform-load pipeline, crawls, parses and word breaks large corpuses of documents in database tables. Documents can be broken into tuples. The tuples can be sent to a heuristically based algorithm that uses statistical language models and weight+cross-entropy threshold functions to summarize the document into its “top N” most statistically significant phrases. Accordingly, embodiments of the invention scale efficiently (e.g., linearly) and (potentially large numbers of) documents can be characterized by salient and relevant key phrases (tags).