Abstract
This paper introduces an automatic method to extend existing WordNets via machine translation. Our proposal relies on the hierarchical skeleton of the English Princeton WordNet (PWN) as a backbone to extend their taxonomies. Our proposal is applied to the Arabic WordNet (AWN) to enrich it by adding new synsets, and also by providing vocalizations and usage examples for each inserted lemma. Around 12000 new potential synsets can be added to AWN with a precision of at least \(93\%\). As such the coverage of AWN in terms of synsets can be increased from 11269 to around 24000 a very promising achievement on the path of enriching the Arabic WordNet.
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Notes
- 1.
- 2.
The version of AWN used in this work is available in this link http://globalwordnet.org/arabic-wordnet/awn-browse/.
- 3.
A lemma is said to be non-ambiguous if it appears in only one synset; it is considered ambiguous otherwise.
- 4.
- 5.
- 6.
- 7.
The vocalization toolkit is available at https://github.com/Ycfx/Arabic-Diacritizer under the GNU License.
- 8.
We vocalize the entire sentence to obtain a more reliable vocalization since the vocalization model proposed in [22] uses a statistical model which tends to give better results when a wider context is available.
- 9.
The WordNet taxonomy is not a strict hierarchy; in other words, a synset in the taxonomy can have multiple parents.
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- 12.
The code source for fast_alig is publicly available at http://github.com/clab/fastalign.
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Hadj Ameur, M.S., Khadir, A.C., Guessoum, A. (2018). An Automatic Approach for WordNet Enrichment Applied to Arabic WordNet. In: Lachkar, A., Bouzoubaa, K., Mazroui, A., Hamdani, A., Lekhouaja, A. (eds) Arabic Language Processing: From Theory to Practice. ICALP 2017. Communications in Computer and Information Science, vol 782. Springer, Cham. https://doi.org/10.1007/978-3-319-73500-9_1
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