Discourse Analysis for Forum Issue Categorization
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Solution Overview
Problem
Existing systems face challenges in transforming unstructured and noisy data from online forums into a usable form for knowledge bases, particularly in identifying and categorizing issues related to product modifications or new devices, which hampers the effectiveness of automated customer support and knowledge enrichment.
Innovation Solution
A discourse analysis system and method that utilizes a domain term vocabulary and discourse patterns to categorize text sequences, distinguishing between anomaly and information request categories, enabling the extraction of relevant information and updating knowledge bases accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated question answering systems are developed from scratch with structured knowledge bases, then customer support capability is improved, but time consumption and development cost increase significantly
Solution Approach 1:
The system performs preliminary action by automatically mining and processing forum data in advance to build and update the knowledge base. Forum posts are continuously crawled, analyzed, and structured before being incorporated into the knowledge base, so that when customer queries arise, the system already has up-to-date information ready to use, eliminating the need for manual knowledge base creation and updates.
Solution Approach 2:
The system enables self-service by automatically extracting questions and answers from forum data and populating the knowledge base without human intervention. The automated pipeline includes crawling forum posts, identifying Q&A pairs, structuring them into standardized formats, and updating the knowledge base autonomously, allowing the system to maintain itself and continuously improve without requiring manual effort.
2Quantity of substance
If forum data is mined to enrich knowledge bases, then knowledge coverage is improved, but data quality and reliability deteriorate due to noisy and unstructured nature
Solution Approach 1:
The system applies the extraction principle by selectively pulling out relevant information from noisy forum data. It identifies and extracts specific Q&A pairs from unstructured forum posts, separating useful knowledge from irrelevant content through pattern matching and natural language processing, thereby enriching the knowledge base with high-quality extracted information while discarding noise.
Solution Approach 2:
The system uses an intermediary processing layer between raw forum data and the knowledge base. This intermediary includes multiple processing steps: crawling forum posts, identifying Q&A structures, validating content quality, structuring data into standardized formats, and filtering for relevance. This intermediary layer transforms noisy unstructured data into reliable structured knowledge suitable for the knowledge base.
3Measurement precision
If discourse analysis is applied to categorize forum posts, then information extraction accuracy is improved, but system complexity increases
Solution Approach 1:
The system applies segmentation by breaking down the complex task of forum post analysis into distinct manageable components: crawling forum data, identifying Q&A pairs, categorizing issues by type, extracting domain terms, and structuring output. Each component is handled by a separate module or process step, making the overall complex system manageable and maintainable while achieving high categorization accuracy through specialized processing at each stage.
Data Source
AI summary
A method for categorizing an issue includes, for each of a plurality of categories of issue, providing at least one discourse pattern for identify text sequences that meet the discourse pattern. At least one of the discourse patterns specifies that an instance of a domain term in a domain term vocabulary be present in the text sequence for the pattern to be met. An issue is received which includes a text sequence. The text sequence is categorized based on which, if any, of the discourse patterns are met by the text sequence of the received issue. Information based on the categorization of the text sequence is output.