A survey of related studies was conducted by the researchers in order to provide more insight into the research in the field of an experimentation and to get support of the Borer-Moore string searching algorithm as a relevant string matching algorithm that can be integrated with Natural Language Processing method and why it creates a better string searching process. The available literature related to the research work has been reviewed and presented under two distinct heads biz.
String Searching Algorithm ii) Natural Language Processing 2. 1. String Searching Algorithm There are many existing string matching algorithms, and each is efficient and effective in one way or another. It is worth noting that string is used interchangeably with text. It is a sequence of characters that may be a set of alphabet. The researchers have selected the Borer-Moore string matching algorithm because it is used in most software applications.
String matching algorithms work by matching two strings, the main string and the pattern. The main string is larger than or equal to the pattern that is the text being searched. Borer-Moore String matching algorithm works by comparing from right to left. It is fast because it skips some of the characters. It is efficient because with each failed attempt to match between the search string and the pattern, it uses the gathered information from that attempt to rule out as many positions where the pattern does not match. REF_002] It becomes faster if the set of alphabet is larger and the pattern is longer
The current areas covered by natural language processing are automatic summarization, coherence resolution, discourse analysis, machine translation, morphological segmentation, named entity recognition, natural engage generation, natural language understanding, optical character recognition, sentence breaking, sentiment analysis, speech recognition, speech segmentation, topic segmentation, word segmentation, word sense disambiguation, information retrieval, information extraction and speech processing; some other are stemming, text simplification, text-to-speech, text-proofing, natural language search, query expansion, automated essay scoring and truncating
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