Rule Extraction in Machine Learning: Chat Incremental Pattern Constructor

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 construction. The updated implementation is also reviewed in detail: it parses an embedded dictionary, normalizes lexical keys, caches definition tokens and part-of-speech tags, computes Jaccard scores on bitsets, applies heuristic linguistic bonuses, and persists the knowledge base with a versioned binary format. The paper emphasizes mathematical formulation and algorithmic clarity, and it provides pseudocode for the learning, scoring, and construction pipeline.