Abstract
In this paper we introduce a minimalist hypothesis for keywords extraction: keywords can be extracted from text documents by considering concepts underlying document terms. Furthermore, central concepts are individuated as the concepts that are more related to title concepts. Namely, we propose five metrics, that are diverse in essence, to compute the centrality of concepts in the document body with respect to those in the title. We finally report about an experimentation over a popular data set of human annotated news articles; the results confirm the soundness of our hypothesis.
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Notes
- 1.
- 2.
There is a subtle though neat difference between semantic relatedness and similarity: consider, e.g., that ‘eraser’ and ‘pencil’ are related but not similar, whilst ‘pencil’ and ‘pen’ are similar.
- 3.
For the sake of clarity in this example we consider the lexical rather than the unified vector, i.e. having terms in place of conceptual IDs that are actually used by the system.
- 4.
In order to compute such measures we used the Palmetto library [16].
- 5.
Specifically, in the Palmetto implementation, the pointwise mutual information (PMI) and word co-occurrence counts were computed by using Wikipedia as reference corpus [16].
- 6.
The ttcs \(^{\mathcal {E}}\) resource is available for download at the URL http://ttcs.di.unito.it.
- 7.
Available at the URLs http://www.alchemyapi.com/api/keyword-extraction/, http://developer.zemanta.com/, http://www.opencalais.com/, http://TagMe.di.unipi.it/ and http://www.textrazor.com/, respectively.
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Acknowledgements
We desire to thank Simone Donetti and the Technical Staff of the Computer Science Department of the University of Turin, for their support in the setup and administration of the computer system used in the experimentation.
The authors are also grateful to the anonymous reviewers for their valuable comments and suggestions.
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Colla, D., Mensa, E., Radicioni, D.P. (2017). Semantic Measures for Keywords Extraction. In: Esposito, F., Basili, R., Ferilli, S., Lisi, F. (eds) AI*IA 2017 Advances in Artificial Intelligence. AI*IA 2017. Lecture Notes in Computer Science(), vol 10640. Springer, Cham. https://doi.org/10.1007/978-3-319-70169-1_10
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