A New Approach for Measuring Semantic Similarity in Ontology and Its Application in Information Retrieval

Wagh, Kishor; Kolhe, Satish R.

doi:10.1007/978-3-642-35455-7_12

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…On the basis of above, a generalized regression neural network was established to compute semantic similarity. Lin's algorithm [11] was chosen in the comparison experiments, as it ran compatibly and steadily in much different ontology. The Root Mean Square Error (RMSE) and Correlation coefficient were algorithm evaluation criterions.…”

Section: Resultsmentioning

confidence: 99%

Semantic Similarity Algorithm Based on Generalized Regression Neural Network

Cao¹,

Wu²,

Wang³

et al. 2015

Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy

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Abstract. Based on the intensives study of semantic similarity algorithms and artificial neural networks knowledge, a generalized regression neural network semantic similarity algorithm is proposed. Training samples are obtained by extracting the principal component of semantic similarity influence factors; the desired spread factor and best training sample sets are gotten by crossvalidation and recursive optimization; a generalized regression neural network is established with these supports. Experiment comparison and analysis verify that, the result of semantic similarity algorithm based on generalized regression neural network is more accurate than that of existing algorithms.

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Section: Resultsmentioning

confidence: 99%

Semantic Similarity Algorithm Based on Generalized Regression Neural Network

Cao¹,

Wu²,

Wang³

et al. 2015

Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy

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“…Budanitsky and Hirst (2006) review methods to determine semantic relatedness. Newer examples for WordNet-based calculation of semantic similarity are the works by Qin et al (2009), Cai et al (2010), Liu et al (2012), and Wagh and Kolhe (2012).…”

Section: Wordnet-based Semantic Similarity Calculationmentioning

confidence: 99%

Using Ellipsis Detection and Word Similarity for Transformation of Spoken Language into Grammatically Valid Sentences

Giuliani¹,

Marschall²,

Isard³

2014

Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

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When humans speak they often use grammatically incorrect sentences, which is a problem for grammar-based language processing methods, since they expect input that is valid for the grammar. We present two methods to transform spoken language into grammatically correct sentences. The first is an algorithm for automatic ellipsis detection, which finds ellipses in spoken sentences and searches in a combinatory categorial grammar for suitable words to fill the ellipses. The second method is an algorithm that computes the semantic similarity of two words using WordNet, which we use to find alternatives to words that are unknown to the grammar. In an evaluation, we show that the usage of these two methods leads to an increase of 38.64% more parseable sentences on a test set of spoken sentences that were collected during a human-robot interaction experiment.

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