PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Jimenez Zafra S., Berardi G., Esuli A., Marcheggiani D., Martin-Valdivia M. T., Moreo Fernández A. A Multi-lingual Annotated Dataset for Aspect-Oriented Opinion Mining. In: EMNLP - Conference on Empirical Methods in Natural Language Processing (Lisbon, September 17-21 2015). Proceedings, pp. 2533 - 2538. The Association for Computational Linguistics, 2015.
 
 
Abstract
(English)
We present the Trip-MAML dataset, a Multi-Lingual dataset of hotel reviews that have been manually annotated at the sentence-level with Multi-Aspect sentiment labels. This dataset has been built as an extension of an existent English-only dataset, adding documents written in Italian and Spanish. We detail the dataset construction process, covering the data gathering, selection, and annotation. We present inter-annotator agreement figures and baseline experimental results, comparing the three languages. Trip-MAML is a multi-lingual dataset for aspect-oriented opinion mining that enables researchers (i) to face the problem on languages other than English and (ii) to the experiment the application of cross-lingual learning methods to the task
URL: http://www.aclweb.org/anthology/D/D15/D15-1302.pdf
Subject multilingual
opinion mining
aspect mining
I.2.6 ARTIFICIAL INTELLIGENCE. Learning


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