Reducing the false positive rate requires optimization at both the algorithmic and operational levels:
- context-sensitive: Analyze the context in combination with NLP techniques, e.g., excluding sensitive word combinations in nouns (e.g., 'Beijing' in 'Peking University') through lexical annotation.
- Whitelisting mechanism: Create a whitelist of common misclassified words (e.g., brand names, place names) and prioritize them over sensitive word pool matches.
- hierarchical filtration: Enable strict matching for high-risk words such as political categories, and allow partial character spacing for low-risk words (such as the regular
色.{0,2}情).
It is recommended to analyze the miscarriage of justice logs on a regular basis and adjust the thesaurus and rules in a targeted manner to balance security and user experience.
This answer comes from the articleSensitive-lexicon: a continuously updated thesaurus of Chinese sensitive wordsThe
May not be reproduced without permission:AI productivity tools " Practical ways to improve the false positive rate of sensitive word filtering
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