A Hybrid Length-Based Pattern Matching Algorithm for Text Searching

This paper presents a hybrid algorithm for pattern matching in text, which combines word length preprocessing with the Knuth-Morris-Pratt (KMP) algorithm. Its performance was evaluated against KMP and Boyer-Moore (BM) in two scenarios: synthetic texts and real-world texts. In the former, classical algorithms proved more efficient due to the uniform structure of the data. However, in real-world texts, the hybrid algorithm significantly reduced search times, thanks to its ability to filter matches by length patterns before performing character-by-character comparisons. The algorithm also demonstrated flexibility in recognizing patterns with different delimiters. Among its limitations is the difficulty in detecting substrings within longer words. As future work, the incorporation of partial matching techniques and the adaptation of the approach to multilingual environments and machine learning systems are proposed. The dataset used is provided to encourage reproducibility.