Research Article
Semantic Similarity Assessment using Universal Networking Language
@INPROCEEDINGS{10.4108/eai.7-12-2021.2314540, author={A Chitra and Anupriya Rajkumar}, title={Semantic Similarity Assessment using Universal Networking Language}, proceedings={Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India}, publisher={EAI}, proceedings_a={ICCAP}, year={2021}, month={12}, keywords={paraphrase recognition cross language similarity support vector machine}, doi={10.4108/eai.7-12-2021.2314540} }- A Chitra
Anupriya Rajkumar
Year: 2021
Semantic Similarity Assessment using Universal Networking Language
ICCAP
EAI
DOI: 10.4108/eai.7-12-2021.2314540
Abstract
Semantic similarity assessment is a key problem in Natural Language Understanding which finds wide application in Information Retrieval and Extraction. Determining whether two natural language text units are semantically equivalent is a challenging task. In this work, a machine learning approach based on matching of Universal Networking Language (UNL) forms has been proposed for semantic similarity assessment. Features which measure the relatedness of the UNL forms are used as input to a Support Vector Machine classifier to determine semantic equivalence. The performance of the system has been evaluated on the Microsoft Research Paraphrase Corpus with an accuracy of 71%. The suitability of the UNL matching scheme for handling multi-lingual inputs has also been demonstrated.
