both of these approaches use NFAs under the hood, which means O(m * n) matching. our approach is fundamentally different: we encode lookaround information directly in the automaton via derivatives, which gives us O(n) matching with a small constant. the trade-off is that we restrict lookarounds to a normalized form (?<=R1)R2(?=R3) where R1/R2/R3 themselves don’t contain lookarounds. the oracle-based approaches support more general nesting, but pay for it in the matching loop. one open question i have is how they handle memory for the oracle table - if you read a gigabyte of text, do you keep a gigabyte-sized table in memory for each lookaround in the pattern?
Карина Черных (Редактор отдела «Ценности»)，更多细节参见体育直播
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硬币的另一面，则是媒体行业的生门。当且仅当 AI 剥离了所有重复性的文字劳作之后，真正的新闻专业主义，才会迎来重塑内核的契机。，推荐阅读搜狗输入法下载获取更多信息
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