Detecting Indonesian ambiguous sentences using Boyer-Moore algorithm

Ambiguous sentences are divided into 3 types namely phonetic, lexical, and grammatical. This study focuses on grammatical ambiguous sentences, grammatical ambiguous sentences are ambiguities that occur due to incorrect grammar, but this ambiguity will disappear once it is used within a sentence.  Ambiguous sentences become a big problem when they are processed by a computer. In order for the computer to interpret ambiguous words correctly, this study seeks to develop detection of Indonesian ammbiguous sentences using Boyer Moore algorithm. This algorithm matches ambiguous sentences that are inserted as input with the data set. Then the sentence is being detected whether it contains ambiguous sentences, by calculating the percentage of similarity using cosine similarity method. Cosine similarity system is able to find out the meaning of the sentence. In the data set, the number of ambiguous sentences that can be collected is 50 words. The 50 words consist of ambiguous words data, ambiguous sentences, and ambiguous sentence meanings. This system trial was carried out for 200 times and the accuracy level was 0.935, precision was 0.9320, and Recall was 0.8. While the F-Measure was 0.8061. While the speed for word search 0.003275 seconds

On Obtaining the Boyer-Moore String-Matching Algorithm by Partial Evaluation ∗

We present the first derivation of the search phase of the Boyer-Moore stringmatching algorithm by partial evaluation of an inefficient string matcher. The derivation hinges on identifying the bad-character-shift heuristic as a bindingtime improvement, bounded static variation. An inefficient string matcher incorporating this binding-time improvement specializes into the search phase of the Horspool algorithm, which is a simplified variant of the Boyer-Moore algorithm. Combining the bad-character-shift binding-time improvement with our previous results yields a new binding-time-improved string matcher tha

An Analytical Approach to Programs as Data Objects

This essay accompanies a selection of 32 articles (referred to in bold face in the text and marginally marked in the bibliographic references) submitted to Aarhus University towards a Doctor Scientiarum degree in Computer Science.The author's previous academic degree, beyond a doctoral degree in June 1986, is an "Habilitation à diriger les recherches" from the Université Pierre et Marie Curie (Paris VI) in France; the corresponding material was submitted in September 1992 and the degree was obtained in January 1993.The present 32 articles have all been written since 1993 and while at DAIMI.Except for one other PhD student, all co-authors are or have been the author's students here in Aarhus

Exact string matching algorithms for searching DNA and protein sequences and searching chemical databases

The enormous quantities of biological and chemical files and databases are likely to grow year on year, consequently giving rise to the need to develop string-matching algorithms capable of minimizing the searching response time. Being aware of this need, this thesis aims to develop string matching algorithms to search biological sequences and chemical structures by studying exact string matching algorithms in detail. As a result, this research developed a new classification of string matching algorithms containing eight categories according to the pre-processing function of algorithms and proposed five new string matching algorithms; BRBMH, BRQS, Odd and Even algorithm (OE), Random String Matching algorithm (RSMA) and Skip Shift New algorithm (SSN). The main purpose behind the proposed algorithms is to reduce the searching response time and the total number of comparisons. They are tested by comparing them with four well- known standard algorithms, Boyer Moore Horspool (BMH), Quick Search (QS), TVSBS and BRFS. This research applied all of the algorithms to sample data files by implementing three types of tests. The number of comparison tests showed a substantial difference in the number of comparisons our algorithms use compared to the non-hybrid algorithms such as QS and BMH. In addition, the tests showed considerable difference between our algorithms and other hybrid algorithm such as TVSBS and BRFS. For instance, the average elapsed search time tests showed that our algorithms presented better average elapsed search time than the BRFS, TVSBS, QS and BMH algorithms, while the average number of tests showed better number of attempts compared to BMH, QS, TVSBS and BRFS algorithms. A new contribution has been added by this research by using the fastest proposed algorithm, the SSN algorithm, to develop a chemical structure searching toolkit to search chemical structures in our local database. The new algorithms were paralleled using OpenMP and MPI parallel models and tested at the University of Science Malaysia (USM) on a Stealth Cluster with different number of threads and processors to improve the speed of searching pattern in the given text which, as we believe, is another contribution