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Journal of Intelligent Information Systems
Erschienen in: Journal of Intelligent Information Systems 3/2017

28.11.2016

Sentence similarity based on semantic kernels for intelligent text retrieval

verfasst von: Samir Amir, Adrian Tanasescu, Djamel A. Zighed

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 3/2017

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Abstract

We propose a new approach to compute semantic similarity between sentences. It is based on the semantic kernel, composed of subject, verb, and object that, we suppose, summarize the general meaning of each sentence. Thanks to linguistics resources available such as Stanford Parser, many features are then extracted from the semantic kernels and aggregated by mean of weights. The weighting is produced by a supervised machine learning technique on a training data set provided by human experts as ground truth. The cross validation shows good performances. Thanks to this similarity measure between sentences, one can build an intelligent text retrieval engine more sensitive to the semantic content, specifically suited for short texts than the classical methods based on bag of words. An application is being developed for highlighting parts of speech in scientific articles.
Vorheriger Artikel An efficient and large-scale reasoning method for the semantic Web

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Metadaten
Titel
Sentence similarity based on semantic kernels for intelligent text retrieval
verfasst von
Samir Amir
Adrian Tanasescu
Djamel A. Zighed
Publikationsdatum
28.11.2016
Verlag
Springer US
Erschienen in
Journal of Intelligent Information Systems / Ausgabe 3/2017
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI
https://doi.org/10.1007/s10844-016-0434-3

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