通过统计锚定优化精确字符串匹配
Optimizing Exact String Matching via Statistical Anchoring
AI总结：
本文提出通过统计锚定优化精确字符串匹配，利用语言统计信息提升模式匹配效率。
摘要：
在本文中，我们提出了一种改进的Boyer-Moore-Horspool算法，专门针对自然语言文本进行优化。该方法涉及对搜索模式进行预处理，以识别其统计上最不频繁的字符，称为
英文摘要：
In this work, we propose an enhancement to the Boyer-Moore-Horspool algorithm tailored for natural language text. The approach involves preprocessing the search pattern to identify its statistically least frequent character, referred to as the "anchor." During the search, verification is first performed at this high-entropy position, allowing the algorithm to quickly discard non-matching windows. This fail-fast strategy reduces unnecessary comparisons, improving overall efficiency. Our implementation shows that incorporating basic linguistic statistics into classical pattern-matching techniques can boost performance without increasing complexity to the shift heuristics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
