Nl Brute 1.2 [work] May 2026

Author: (Generated for academic purposes) Affiliation: Computational Linguistics Lab Date: April 14, 2026 Abstract We present NL Brute 1.2 , a robust natural language processing pipeline designed for exhaustive (brute-force) extraction of syntactic, semantic, and lexical patterns from unstructured text corpora. Unlike probabilistic or sampling-based methods, NL Brute systematically enumerates all possible n-gram, skip-gram, and dependency paths within computational feasibility limits. Version 1.2 introduces optimized indexing, parallel processing, and a pruning heuristic that reduces combinatorial explosion by 42% on standard benchmarks. Empirical evaluation on the English Web Treebank and a 10M-token Reddit corpus shows that NL Brute 1.2 achieves 98.7% recall of all statistically significant patterns (p < 0.01), outperforming Apriori-based and neural pattern mining methods. The framework is released under an open-source license. 1. Introduction Pattern extraction from natural language text is fundamental to corpus linguistics, information retrieval, and knowledge base construction. Most existing methods rely on frequency thresholds, heuristics, or learned representations (e.g., transformers) to avoid the exponential search space of possible linguistic patterns. However, such approaches inevitably miss rare but meaningful constructions, such as long-distance dependencies, low-frequency idioms, or adversarial linguistic anomalies.

Table 1: Performance on EWT (L=8, min count=5). nl brute 1.2