Document Enrichment with Conversation Context for Search Relevance
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Solution Overview
Problem
Information retrieval systems face challenges in returning highly relevant search results, as they often rely solely on document content without effectively utilizing contextual information from conversations that reference common problem resolutions.
Innovation Solution
A method and system that analyze multiple conversation texts to calculate importance scores for messages, cluster them, and enrich electronic documents with aggregated importance scores from relevant conversations, allowing IR systems to search and return more relevant results by incorporating contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If information retrieval systems rely solely on document content for searching, then the search process remains simple and fast, but the relevance of search results deteriorates because contextual information from conversations is not utilized
Solution Approach 1:
The system performs preliminary actions by pre-processing conversation texts before search queries are submitted. It calculates importance scores for messages, clusters them into topics, and stores this structured contextual information in advance. When a search is executed, the pre-processed conversation data is quickly retrieved and combined with document content, improving result relevance without adding significant complexity to the search process itself.
Solution Approach 2:
The system segments conversation texts into individual messages and further groups them into clusters based on importance scores and topical similarity. This segmentation transforms unstructured conversation data into organized, searchable units that can be efficiently integrated with document content, enhancing search relevance while maintaining manageable processing complexity.
2Reliability
If multiple conversation texts are analyzed and processed to extract contextual information, then the relevance of search results improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs computationally intensive operations—such as calculating importance scores for each message, clustering messages into topics, and structuring conversation data—in advance, before actual search queries are received. This pre-processing creates ready-to-use indexed structures that can be quickly queried, thereby improving result accuracy while minimizing the time penalty during actual search operations.
Solution Approach 2:
The system applies partial processing by selectively analyzing and clustering only the most important messages based on pre-calculated importance scores, rather than processing every message in detail. This approach captures sufficient contextual information to improve search accuracy while avoiding the excessive processing time that would result from analyzing all conversation data equally.
3Measurement precision
If conversation texts are clustered into multiple bins with aggregated importance scores, then the precision of information retrieval improves, but the complexity of data structure increases
Solution Approach 1:
The system segments conversation data into discrete message units and further groups them into clusters or bins based on topical similarity and importance. Each cluster contains structured information about related messages, their importance scores, and contextual relationships. This segmentation enables precise matching of search queries with relevant conversation segments while maintaining a manageable data structure through hierarchical organization.
Solution Approach 2:
The system applies local quality by assigning different importance scores and structural treatments to different segments of conversation data based on their relevance and informational value. High-importance messages receive more detailed processing and are positioned prominently in the clustered structure, while less important messages are grouped more generally. This differentiated approach improves information matching precision without uniformly increasing the complexity of the entire data structure.
Data Source
AI summary
A method including: Obtaining multiple conversation texts, one text per conversation, wherein each of the multiple conversation texts comprises: multiple messages authored by multiple parties, and a reference to an electronic document that provides resolution of a problem that is common to all the conversations. Calculating an importance score for each of the multiple messages of all the conversation texts. Clustering the multiple messages of all the conversation texts into multiple bins. Calculating an aggregated importance score for each of the multiple bins, based on the importance scores of the messages contained in the respective bin. Enriching (a) the electronic document, or (b) a record of the electronic document in an index of electronic documents, with at least some of the multiple bins and their aggregated importance scores, wherein the at least some of the multiple bins are added as fields to the electronic document or to the record.