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. This technique
can also apply to large scale dataset to compute term similarity.
%0 Journal Article
%1 kulkarnimeasure
%A Kulkarni, D. M.
%D 2021
%J BOHR International Journal of Intelligent Instrumentation and Computing
%K Clustering Multiwordexpression Semanticnetwork Termsimilarity
%N 1
%P 5-9
%R https://doi.org/10.54646/bijiiac.002
%T Measure Term Similarity Using a Semantic Network Approach
%U https://www.bohrpub.com/article/BIJIIAC/Vol1N1/BIJIIAC_20211102.pdf
%V 1
%X 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. This technique
can also apply to large scale dataset to compute term similarity.
@article{kulkarnimeasure,
abstract = {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. This technique
can also apply to large scale dataset to compute term similarity.},
added-at = {2022-05-05T15:27:12.000+0200},
author = {Kulkarni, D. M.},
biburl = {https://www.bibsonomy.org/bibtex/231e03b42045890f2f5140b1b1715fa5c/ijiiacjournal},
doi = {https://doi.org/10.54646/bijiiac.002},
interhash = {d25d2b8f518f3a2fa40d8d1ecc69fa6d},
intrahash = {31e03b42045890f2f5140b1b1715fa5c},
journal = {BOHR International Journal of Intelligent Instrumentation and Computing},
keywords = {Clustering Multiwordexpression Semanticnetwork Termsimilarity},
language = {English},
month = {July},
number = 1,
pages = {5-9},
timestamp = {2022-05-05T15:27:12.000+0200},
title = {Measure Term Similarity Using a Semantic Network Approach},
url = {https://www.bohrpub.com/article/BIJIIAC/Vol1N1/BIJIIAC_20211102.pdf},
volume = 1,
year = 2021
}