TL;DR
This paper introduces ChatIPC, a lightweight incremental rule extraction system that constructs human-readable rules from text by operating over token graphs and using similarity-guided candidate selection.
Contribution
It formalizes a novel rule extraction method operating over token graphs, with detailed algorithms, heuristics, and an implementation for interpretable text-based rule learning.
Findings
ChatIPC effectively extracts ordered token-transition rules from text.
The system enriches rules with definition-based expansion and similarity-guided response construction.
Implementation details include parsing dictionaries, caching tokens, and applying linguistic heuristics.
Abstract
Rule extraction is a central problem in interpretable machine learning because it seeks to convert opaque predictive behavior into human-readable symbolic structure. This paper presents Chat Incremental Pattern Constructor (ChatIPC), a lightweight incremental symbolic learning system that extracts ordered token-transition rules from text, enriches them with definition-based expansion, and constructs responses by similarity-guided candidate selection. The system may be viewed as a rule extractor operating over a token graph rather than a conventional classifier. I formalize the knowledge base, definition expansion, candidate scoring, repetition control, English-rule heuristics, and response construction mechanisms used by ChatIPC. I further situate the method within the literature on rule extraction, decision tree induction, association rules, interpretable machine learning, and sequence…
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