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Taxonomy-based information content and wordnet-wiktionary-wikipedia glosses for semantic relatedness

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Abstract

Computing the semantic similarity/relatedness between terms is an important research area for several disciplines, including artificial intelligence, cognitive science, linguistics, psychology, biomedicine and information retrieval. These measures exploit knowledge bases to express the semantics of concepts. Some approaches, such as the information theoretical approaches, rely on knowledge structure, while others, such as the gloss-based approaches, use knowledge content. Firstly, based on structure, we propose a new intrinsic Information Content (IC) computing method which is based on the quantification of the subgraph formed by the ancestors of the target concept. Taxonomic measures including the IC-based ones consume the topological parameters that must be extracted from taxonomies considered as Directed Acyclic Graphs (DAGs). Accordingly, we propose a routine of graph algorithms that are able to provide some basic parameters, such as depth, ancestors, descendents, Lowest Common Subsumer (LCS). The IC-computing method is assessed using several knowledge structures which are: the noun and verb WordNet “is a” taxonomies, Wikipedia Category Graph (WCG), and MeSH taxonomy. We also propose an aggregation schema that exploits the WordNet “is a” taxonomy and WCG in a complementary way through the IC-based measures to improve coverage capacity. Secondly, taking content into consideration, we propose a gloss-based semantic similarity measure that operates based on the noun weighting mechanism using our IC-computing method, as well as on the WordNet, Wiktionary and Wikipedia resources. Further evaluation is performed on various items, including nouns, verbs, multiword expressions and biomedical datasets, using well-recognized benchmarks. The results indicate an improvement in terms of similarity and relatedness assessment accuracy.

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Notes

  1. http://www.nlm.nih.gov/mesh.

  2. https://en.wiktionary.org/wiki/Wiktionary:Main_Page.

  3. http://nlp.stanford.edu/software/.

  4. card Leaves<n=∣E∣ that represents the cardinality of the set of nodes.

  5. http://adapt.seiee.sjtu.edu.cn/similarity/.

  6. http://www.cl.cam.ac.uk/~fh295/simlex.html..

  7. http://www.technion.ac.il/~kirar/Datasets.html.

  8. The Amazon Mechanical Turk (MTurk) is an online labor market where workers are paid small amounts of money to complete small tasks. https://www.mturk.com/mturk/welcome.

  9. http://clic.cimec.unitn.it/elia.bruni/MEN.htm.

  10. http://wacky.sslmit.unibo.it/doku.php?id=corpora.

  11. http://www-nlp.stanford.edu/~lmthang/morphoNLM/.

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Acknowledgments

The authors would like to express their gratitude to Mr. Anouar Smaoui from the English Language Unit at the SfaxFaculty of Sciences for his constructive language editing and proofreading services.

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  1. Multimedia Information System and Advanced Computing Laboratory, Sfax University, Sfax, 3021, Tunisia

    Mohamed Ben Aouicha, Mohamed Ali Hadj Taieb & Abdelmajid Ben Hamadou

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  1. Mohamed Ben Aouicha
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Correspondence to Mohamed Ali Hadj Taieb.

Appendix: An example of application of the Algorithm 1

Appendix: An example of application of the Algorithm 1

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Aouicha, M.B., Hadj Taieb, M.A. & Hamadou, A.B. Taxonomy-based information content and wordnet-wiktionary-wikipedia glosses for semantic relatedness. Appl Intell 45, 475–511 (2016). https://doi.org/10.1007/s10489-015-0755-x

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