Document-Centric Concept Profiles for Citation-Based Search
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Solution Overview
Problem
Current methods for identifying documents based on citation history are inefficient, requiring significant user effort to find the most frequently cited cases for specific legal concepts, as they rely on manual searches and lack effective semantic matching.
Innovation Solution
A system that utilizes document-centric concept profiles, where each document is associated with a set of concepts and reference values calculated by tabulating citations, allowing for automatic normalization and surfacing of documents with the highest reference values for given concepts, thereby prioritizing highly cited documents in search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual search methods are used to find frequently cited cases for specific legal concepts, then users can identify relevant documents, but user effort and time consumption increase significantly
Solution Approach 1:
The system pre-calculates and stores document-centric concept profiles that include tabulated citation counts for multiple legal concepts before queries are submitted. This preliminary processing of citation data allows the system to quickly retrieve pre-computed results without performing manual searches at query time, thereby maintaining high accuracy while significantly reducing user time investment
Solution Approach 2:
The system automatically computes reference values by tabulating citation data and generates ranked lists of frequently cited documents without requiring user intervention. The automated normalization of legal terms and concepts further reduces manual effort, allowing the system to serve itself in identifying and ranking relevant documents based on pre-processed citation histories
2Measurement precision
If comprehensive citation analysis is performed across the entire corpus, then the most relevant documents can be identified, but system complexity and processing requirements increase
Solution Approach 1:
The system divides the citation analysis task into separate document-centric concept profiles for each document in the corpus. Each profile independently tabulates citation counts for specific legal concepts, allowing the system to process citation data in manageable segments rather than analyzing the entire corpus as a single complex unit. This segmentation maintains comprehensive analysis accuracy while reducing overall system complexity
Solution Approach 2:
The system pre-computes and stores normalized concept profiles with tabulated citation counts for all documents before queries are submitted. This preliminary processing transforms the complex citation analysis into pre-organized data structures that can be quickly retrieved and compared, reducing the computational complexity required at query time while maintaining comprehensive analysis accuracy
Data Source
AI summary
Systems, methods, and computer-executable instructions for identifying a document are described. A method includes receiving a query from a graphical user interface having one or more concepts, normalizing a set of terms or concepts in the query to create a normalized query, comparing the normalized query to a set of document centric concept profiles associated with a set of documents in a corpus, where each document centric concept includes a plurality of concepts and at least one reference value for each concept, where the reference value is calculated by tabulating the number of times a document associated with one of the document centric concept profiles is cited by a citing instance for the concept, and surfacing a document from the corpus with the highest reference value for the concept.