Point Anomaly Detection in Text Data Using Semantic Annotation
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Solution Overview
Problem
Current anomaly detection techniques are ineffective in identifying point anomalies in text data, particularly in complex computer systems, often leading to undetected issues due to lack of labeled data and human error in manual detection.
Innovation Solution
A system and method for detecting point anomalies in text data using tokenization, annotation with parameters like part-of-speech, sentiment polarity, and domain rules, and generating inferences with machine learning models, including unsupervised approaches to identify anomaly subjects, types, and actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual anomaly detection is used, then human judgment can be applied to complex scenarios, but detection efficiency is low and anomalies go undetected
Solution Approach 1:
The patent segments the anomaly detection task into multiple independent modules: tokenization module for breaking down text, annotation module for labeling tokens with POS, sentiment, and negation attributes, anomaly detection module for identifying anomalies, and inference module for generating explanations. This segmentation allows each module to specialize in specific functions, improving overall detection accuracy while maintaining processing efficiency through parallel execution.
Solution Approach 2:
The patent introduces an intermediary annotation layer between raw text tokens and anomaly detection. Each token is annotated with multiple attributes (POS, sentiment polarity, negation statements, domain rules) before being fed to the anomaly detection module. This intermediary layer transforms raw text into structured, semantically enriched data, enabling more accurate anomaly identification without requiring direct human analysis of complex text patterns.
2Ease of manufacture
If unsupervised anomaly detection is used, then labeled data is not required, but detection precision is reduced due to lack of ground truth
Solution Approach 1:
The patent performs preliminary annotation of tokens with multiple attributes (POS, sentiment polarity, negation statements, domain rules) before anomaly detection. This preliminary processing enriches the data structure and prepares it for more precise anomaly identification. The annotation step acts as a pre-processing action that enhances the quality of input data to the detection algorithm, improving precision without requiring labeled anomaly data.
Solution Approach 2:
The patent changes the parameter space for anomaly detection by introducing multiple annotation dimensions (POS tags, sentiment polarity scores, negation boolean flags, domain rule matches). Instead of detecting anomalies in raw text space, the system operates in the enriched attribute space where patterns can be more easily identified. This parameter transformation enables unsupervised detection to achieve precision comparable to supervised approaches.
3Reliability
If complex annotation parameters are applied, then anomaly identification accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the complex annotation process into separate, manageable modules: tokenization module for word segmentation, POS tagging module for part-of-speech identification, sentiment analysis module for polarity detection, negation detection module for boolean flag assignment, and domain rule matching module for contextual validation. Each module handles a specific aspect of annotation independently, making the overall complex system modular and easier to implement while maintaining high accuracy through the combination of multiple annotation types.
Data Source
AI summary
A method for detecting point anomaly in a text data is disclosed. The method may include tokenizing text input comprising a plurality of text entities into a plurality of tokens, annotating, by the point anomaly detection device, the plurality of tokens, based on one or more annotation parameters. One or more annotation parameters comprise a part-of-speech, a sentiment polarity, a negation statement, and domain rules. The method further includes identifying from one or more annotated tokens, at least one of: one or more anomaly subject tokens, one or more anomaly type tokens, and one or more action type tokens from plurality of tokens. The method further includes generating inferences based on the identified one or more anomaly type tokens, the one or more action type tokens from the plurality of tokens, and the one or more annotation parameters.