@inproceedings{bouamor-etal-2016-transfer,
title = "Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality",
author = "Bouamor, Dhouha and
Llanos, Leonardo Campillos and
Ligozat, Anne-Laure and
Rosset, Sophie and
Zweigenbaum, Pierre",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1366",
pages = "2312--2316",
abstract = "While measuring the readability of texts has been a long-standing research topic, assessing the technicality of terms has only been addressed more recently and mostly for the English language. In this paper, we train a learning-to-rank model to determine a specialization degree for each term found in a given list. Since no training data for this task exist for French, we train our system with non-lexical features on English data, namely, the Consumer Health Vocabulary, then apply it to French. The features include the likelihood ratio of the term based on specialized and lay language models, and tests for containing morphologically complex words. The evaluation of this approach is conducted on 134 terms from the UMLS Metathesaurus and 868 terms from the Eugloss thesaurus. The Normalized Discounted Cumulative Gain obtained by our system is over 0.8 on both test sets. Besides, thanks to the learning-to-rank approach, adding morphological features to the language model features improves the results on the Eugloss thesaurus.",
}
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%0 Conference Proceedings
%T Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality
%A Bouamor, Dhouha
%A Llanos, Leonardo Campillos
%A Ligozat, Anne-Laure
%A Rosset, Sophie
%A Zweigenbaum, Pierre
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F bouamor-etal-2016-transfer
%X While measuring the readability of texts has been a long-standing research topic, assessing the technicality of terms has only been addressed more recently and mostly for the English language. In this paper, we train a learning-to-rank model to determine a specialization degree for each term found in a given list. Since no training data for this task exist for French, we train our system with non-lexical features on English data, namely, the Consumer Health Vocabulary, then apply it to French. The features include the likelihood ratio of the term based on specialized and lay language models, and tests for containing morphologically complex words. The evaluation of this approach is conducted on 134 terms from the UMLS Metathesaurus and 868 terms from the Eugloss thesaurus. The Normalized Discounted Cumulative Gain obtained by our system is over 0.8 on both test sets. Besides, thanks to the learning-to-rank approach, adding morphological features to the language model features improves the results on the Eugloss thesaurus.
%U https://aclanthology.org/L16-1366
%P 2312-2316
Markdown (Informal)
[Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality](https://aclanthology.org/L16-1366) (Bouamor et al., LREC 2016)
ACL
- Dhouha Bouamor, Leonardo Campillos Llanos, Anne-Laure Ligozat, Sophie Rosset, and Pierre Zweigenbaum. 2016. Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 2312–2316, Portorož, Slovenia. European Language Resources Association (ELRA).