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        <namePart type="given">Surangika</namePart>
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        <namePart type="given">Gihan</namePart>
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            <title>Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016)</title>
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            <namePart type="family">Wu</namePart>
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            <namePart type="given">Pushpak</namePart>
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    <abstract>Currently, corpus based-similarity, string-based similarity, and knowledge-based similarity techniques are used to compare short phrases. However, no work has been conducted on the similarity of phrases in Sinhala language. In this paper, we present a hybrid methodology to compute the similarity between two Sinhala sentences using a Semantic Similarity Measurement technique (corpus-based similarity measurement plus knowledge-based similarity measurement) that makes use of word order information. Since Sinhala WordNet is still under construction, we used lexical resources in performing this semantic similarity calculation. Evaluation using 4000 sentence pairs yielded an average MSE of 0.145 and a Pearson correla-tion factor of 0.832.</abstract>
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%0 Conference Proceedings
%T Sinhala Short Sentence Similarity Calculation using Corpus-Based and Knowledge-Based Similarity Measures
%A Kadupitiya, Jcs
%A Ranathunga, Surangika
%A Dias, Gihan
%Y Wu, Dekai
%Y Bhattacharyya, Pushpak
%S Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F kadupitiya-etal-2016-sinhala
%X Currently, corpus based-similarity, string-based similarity, and knowledge-based similarity techniques are used to compare short phrases. However, no work has been conducted on the similarity of phrases in Sinhala language. In this paper, we present a hybrid methodology to compute the similarity between two Sinhala sentences using a Semantic Similarity Measurement technique (corpus-based similarity measurement plus knowledge-based similarity measurement) that makes use of word order information. Since Sinhala WordNet is still under construction, we used lexical resources in performing this semantic similarity calculation. Evaluation using 4000 sentence pairs yielded an average MSE of 0.145 and a Pearson correla-tion factor of 0.832.
%U https://aclanthology.org/W16-3705/
%P 44-53

Markdown (Informal)

[Sinhala Short Sentence Similarity Calculation using Corpus-Based and Knowledge-Based Similarity Measures](https://aclanthology.org/W16-3705/) (Kadupitiya et al., WSSANLP 2016)

• Sinhala Short Sentence Similarity Calculation using Corpus-Based and Knowledge-Based Similarity Measures (Kadupitiya et al., WSSANLP 2016)

ACL

• Jcs Kadupitiya, Surangika Ranathunga, and Gihan Dias. 2016. Sinhala Short Sentence Similarity Calculation using Corpus-Based and Knowledge-Based Similarity Measures. In Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016), pages 44–53, Osaka, Japan. The COLING 2016 Organizing Committee.