Calculating the semantic similarity between sentences is a long dealt problem
in the area of natural language processing. The semantic analysis field has a
crucial role to play in the research related to the text analytics. The
semantic similarity differs as the domain of operation differs. In this paper,
we present a methodology which deals with this issue by incorporating semantic
similarity and corpus statistics. To calculate the semantic similarity between
words and sentences, the proposed method follows an edge-based approach using a
lexical database. The methodology can be applied in a variety of domains. The
methodology has been tested on both benchmark standards and mean human
similarity dataset. When tested on these two datasets, it gives highest
correlation value for both word and sentence similarity outperforming other
similar models. For word similarity, we obtained Pearson correlation
coefficient of 0.8753 and for sentence similarity, the correlation obtained is
0.8794.
%0 Generic
%1 pawar2018calculating
%A Pawar, Atish
%A Mago, Vijay
%D 2018
%K alignment relatedness semantic semantic-similarity sts
%T Calculating the similarity between words and sentences using a lexical
database and corpus statistics
%U http://arxiv.org/abs/1802.05667
%X Calculating the semantic similarity between sentences is a long dealt problem
in the area of natural language processing. The semantic analysis field has a
crucial role to play in the research related to the text analytics. The
semantic similarity differs as the domain of operation differs. In this paper,
we present a methodology which deals with this issue by incorporating semantic
similarity and corpus statistics. To calculate the semantic similarity between
words and sentences, the proposed method follows an edge-based approach using a
lexical database. The methodology can be applied in a variety of domains. The
methodology has been tested on both benchmark standards and mean human
similarity dataset. When tested on these two datasets, it gives highest
correlation value for both word and sentence similarity outperforming other
similar models. For word similarity, we obtained Pearson correlation
coefficient of 0.8753 and for sentence similarity, the correlation obtained is
0.8794.
@misc{pawar2018calculating,
abstract = {Calculating the semantic similarity between sentences is a long dealt problem
in the area of natural language processing. The semantic analysis field has a
crucial role to play in the research related to the text analytics. The
semantic similarity differs as the domain of operation differs. In this paper,
we present a methodology which deals with this issue by incorporating semantic
similarity and corpus statistics. To calculate the semantic similarity between
words and sentences, the proposed method follows an edge-based approach using a
lexical database. The methodology can be applied in a variety of domains. The
methodology has been tested on both benchmark standards and mean human
similarity dataset. When tested on these two datasets, it gives highest
correlation value for both word and sentence similarity outperforming other
similar models. For word similarity, we obtained Pearson correlation
coefficient of 0.8753 and for sentence similarity, the correlation obtained is
0.8794.},
added-at = {2019-05-16T20:50:17.000+0200},
author = {Pawar, Atish and Mago, Vijay},
biburl = {https://www.bibsonomy.org/bibtex/210d07ccc001e35e9f00976ea26d33f6b/patrickz},
interhash = {73e89749494b7268129af64bcf7d6d01},
intrahash = {10d07ccc001e35e9f00976ea26d33f6b},
keywords = {alignment relatedness semantic semantic-similarity sts},
note = {cite arxiv:1802.05667},
timestamp = {2019-05-16T20:50:17.000+0200},
title = {Calculating the similarity between words and sentences using a lexical
database and corpus statistics},
url = {http://arxiv.org/abs/1802.05667},
year = 2018
}