Exploiting and Evaluating a Supervised, Multilanguage Keyphrase Extraction pipeline for under-resourced languages

Marco Basaldella, Muhammad Helmy, Elisa Antolli, Mihai Horia Popescu, Giuseppe Serra, Carlo Tasso

Abstract
This paper evaluates different techniques for building a supervised, multilanguage keyphrase extraction pipeline for languages which lack a gold standard. Starting from an unsupervised English keyphrase extraction pipeline, we implement pipelines for Arabic, Italian, Portuguese, and Romanian, and we build test collections for languages which lack one. Then, we add a Machine Learning module trained on a well-known English language corpus and we evaluate the performance not only over English but on the other languages as well. Finally, we repeat the same evaluation after training the pipeline over an Arabic language corpus to check whether using a language-specific corpus brings a further improvement in performance. On the five languages we analyzed, results show an improvement in performance when using a machine learning algorithm, even if such algorithm is not trained and tested on the same language.
Anthology ID:
R17-1012
Volume:
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
78–85
Language:
URL:
https://doi.org/10.26615/978-954-452-049-6_012
DOI:
10.26615/978-954-452-049-6_012
Bibkey:
Cite (ACL):
Marco Basaldella, Muhammad Helmy, Elisa Antolli, Mihai Horia Popescu, Giuseppe Serra, and Carlo Tasso. 2017. Exploiting and Evaluating a Supervised, Multilanguage Keyphrase Extraction pipeline for under-resourced languages. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 78–85, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Exploiting and Evaluating a Supervised, Multilanguage Keyphrase Extraction pipeline for under-resourced languages (Basaldella et al., RANLP 2017)
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https://doi.org/10.26615/978-954-452-049-6_012
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