Sliding Window Artificial Language Detection in Text Analysis
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Solution Overview
Problem
Natural language content analysis is compromised by the presence of undetected artificial language, which adds noise and inaccuracies due to its integration within natural language text, making it difficult to distinguish and remove effectively.
Innovation Solution
A computer-implemented method using a sliding window approach to identify and label artificial language within natural language text, allowing for its removal prior to content analysis, utilizing encoding techniques and machine learning models to differentiate between natural and artificial language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content analysis is performed on text containing both natural language and artificial language, then the analysis covers all text content, but the results include noise and inaccuracies from the artificial language portions
Solution Approach 1:
The text is segmented into natural language and artificial language portions using a sliding window approach that divides the text into sequences and applies language detection models to identify boundaries between different language types
Solution Approach 2:
A language detection model serves as an intermediary component that analyzes text sequences and determines whether they contain natural or artificial language, enabling the system to separate and handle different language types appropriately
2Difficulty of detecting and measuring
If markup language tags are used to mark artificial language, then identification and removal is straightforward, but artificial language in plain text form goes undetected
Solution Approach 1:
The system performs preliminary detection of artificial language using language detection models before content analysis, identifying both tagged and plain text artificial language to prevent undetected contamination of analysis results
Solution Approach 2:
The system replaces reliance on mechanical markup tags with intelligent language detection models that can identify artificial language based on linguistic patterns and characteristics, enabling detection of plain text artificial language
3Measurement precision
If a sliding window approach is used to detect artificial language, then detection accuracy improves, but processing time increases
Solution Approach 1:
The sliding window approach applies partial action by analyzing overlapping sequences of text, where each window focuses on a specific portion while sharing information with adjacent windows, achieving thorough detection without redundant full-text analysis
Data Source
AI summary
Examples for detecting and removing non-natural language within natural language to enhance performing content analysis on the natural language are provided herein. A plurality of terms is identified in a phrase, and a sliding window having a defined length is placed over a first sequence of terms from the plurality of terms. The first sequence of terms includes a first term, a second term, and a third term, the first term and the third term being adjacent to the second term. Based on the first term, the second term, and the third term, a determination is made as to whether the second term represents non-natural language. Upon determining that the second term is non-natural language, the second term is labeled as non-natural language and is removed from the plurality of terms based on determining the second term as non-natural language.