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EVALITA. Evaluation of NLP and Speech Tools for Italian

 | 
Pierpaolo Basile
, 
Franco Cutugno
, 
Malvina Nissim
, 
et al.

Part II: EVALITA 2016: Task overviews and participants reports

Tandem LSTM-SVM Approach for Sentiment Analysis

Andrea Cimino y Felice Dell’Orletta

Resumen

In this paper we describe our approach to EVALITA 2016 SENTIPOLC task. We participated in all the subtasks with constrained setting: Subjectivity Classification, Polarity Classification and Irony Detection. We developed a tandem architecture where Long Short Term Memory recurrent neural network is used to learn the feature space and to capture temporal dependencies, while the Support Vector Machines is used for classification. SVMs combine the document embedding produced by the LSTM with a wide set of general–purpose features qualifying the lexical and grammatical structure of the text. We achieved the second best accuracy in Subjectivity Classification, the third position in Polarity Classification, the sixth position in Irony Detection.

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CC-BY-NC-ND-4.0

Únicamente el texto se puede utilizar bajo licencia CC BY-NC-ND 4.0. Salvo indicación contraria, los demás elementos (ilustraciones, archivos adicionales importados) son "Todos los derechos reservados".

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