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Hybrid approach for generating non overlapped substring using genetic algorithmeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Hybrid approach for generating non overlapped substring using genetic algorithmeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Computing semantic similarity measure between words using web search enginecsandit
Semantic Similarity measures between words plays an important role in information retrieval,
natural language processing and in various tasks on the web. In this paper, we have proposed a
Modified Pattern Extraction Algorithm to compute the supervised semantic similarity measure
between the words by combining both page count method and web snippets method. Four
association measures are used to find semantic similarity between words in page count method
using web search engines. We use a Sequential Minimal Optimization (SMO) support vector
machines (SVM) to find the optimal combination of page counts-based similarity scores and
top-ranking patterns from the web snippets method. The SVM is trained to classify synonymous
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Algorithm outperforms by 89.8 percent of correlation value.
Object surface segmentation, Image segmentation, Region growing, X-Y-Z image,...cscpconf
Semantic Similarity measures between words plays an important role in information retrieval, natural language processing and in various tasks on the web. In this paper, we have proposed a Modified Pattern Extraction Algorithm to compute the supervised semantic similarity measure
between the words by combining both page count method and web snippets method. Four association measures are used to find semantic similarity between words in page count method
using web search engines. We use a Sequential Minimal Optimization (SMO) support vector machines (SVM) to find the optimal combination of page counts-based similarity scores and
top-ranking patterns from the web snippets method. The SVM is trained to classify synonymous word-pairs and non-synonymous word-pairs. The proposed Modified Pattern Extraction
Algorithm outperforms by 89.8 percent of correlation value.
Computing semantic similarity between two words comes with variety of approaches. This is mainly
essential for the applications such as text analysis, text understanding. In traditional system search engines are used to compute the similarity between words. In that search engines are keyword based. There is one drawback that user should know what exactly they are looking for. There are mainly two main approaches for computation namely
knowledge based and corpus based approaches. But there is one drawback that these two approaches are not suitable for computing similarity between multi-word expressions. This system provides efficient and effective approach for computing term similarity using semantic network approach. A clustering approach is used in order to improve the
accuracy of the semantic similarity. This approach is more efficient than other computing algorithms. technique
can also apply to large scale dataset to compute term similarity.
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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Computing semantic similarity measure between words using web search enginecsandit
Semantic Similarity measures between words plays an important role in information retrieval,
natural language processing and in various tasks on the web. In this paper, we have proposed a
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Aml efficient approximate membership localization within a web based join framework
1. AML EFFICIENT APPROXIMATE MEMBERSHIP LOCALIZATION WITHIN
A WEB-BASED JOIN FRAMEWORK
ABSTRACT:
In this paper, we propose a new type of Dictionary-based Entity Recognition Problem, named
Approximate Membership Localization (AML). The popular Approximate Membership
Extraction (AME) provides a full coverage to the true matched substrings from a given
document, but many redundancies cause a low efficiency of the AME process and deteriorate the
performance of real-world applications using the extracted substrings.
The AML problem targets at locating non overlapped substrings which is a better approximation
to the true matched substrings without generating overlapped redundancies. In order to perform
AML efficiently, we propose the optimized algorithm P-Prune that prunes a large part of
overlapped redundant matched substrings before generating them.
Our study using several real-word data sets demonstrates the efficiency of P-Prune over a
baseline method. We also study the AML in application to a proposed web-based join framework
scenario which is a search-based approach joining two tables using dictionary-based entity
recognition from web documents. The results not only prove the advantage of AML over AME,
but also demonstrate the effectiveness of our search-based approach.
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