Vector Embeddings for Incident Report Clustering
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Solution Overview
Problem
Existing natural language processing systems for incident reports in IT infrastructure face inefficiencies in text query matching, particularly due to the lack of contextual understanding and high computational costs associated with sparse matrix approaches, which hinder effective similarity analysis and clustering of incident reports.
Innovation Solution
The use of artificial neural networks (ANNs) trained with word vectors and paragraph vectors to provide compact semantic representations of words and text strings, allowing for contextual matching and clustering of incident reports, thereby reducing computational costs and improving relevance in similarity searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sparse matrix approaches are used for text query matching, then comprehensive text analysis is achieved, but computational costs increase and efficiency decreases
Solution Approach 1:
The patent transforms the representation parameters of text data from sparse matrix format to dense vector embeddings. By changing the dimensional representation and density parameters, the system achieves both comprehensive semantic analysis and improved computational efficiency, resolving the contradiction between analysis comprehensiveness and processing speed
Solution Approach 2:
The patent replaces the traditional mechanical sparse matrix computation system with a neural network-based vector embedding system. This substitution enables the system to capture contextual meanings through learned representations while reducing computational complexity and improving matching efficiency
2Loss of information
If word vectors and paragraph vectors are generated for all fields, then semantic representation completeness is improved, but computational cost and processing time increase
Solution Approach 1:
The patent segments the incident report into multiple fields and selectively generates vector representations only for relevant fields. By dividing the text processing task into field-specific segments and applying vector generation only where needed, the system maintains semantic completeness while reducing overall processing time and computational resources
Solution Approach 2:
The patent applies partial action by generating vector representations for only the most relevant fields rather than all fields. This selective approach captures the essential semantic information needed for effective matching while avoiding the computational overhead of processing every field, thus reducing processing time without significantly compromising semantic representation quality
Data Source
AI summary
Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.