Arturo Montejo-Ráez


2019

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SINAI-DL at SemEval-2019 Task 5: Recurrent networks and data augmentation by paraphrasing
Arturo Montejo-Ráez | Salud María Jiménez-Zafra | Miguel A. García-Cumbreras | Manuel Carlos Díaz-Galiano
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes the participation of the SINAI-DL team at Task 5 in SemEval 2019, called HatEval. We have applied some classic neural network layers, like word embeddings and LSTM, to build a neural classifier for both proposed tasks. Due to the small amount of training data provided compared to what is expected for an adequate learning stage in deep architectures, we explore the use of paraphrasing tools as source for data augmentation. Our results show that this method is promising, as some improvement has been found over non-augmented training sets.

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SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions
Miguel A. García-Cumbreras | Salud María Jiménez-Zafra | Arturo Montejo-Ráez | Manuel Carlos Díaz-Galiano | Estela Saquete
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes the participation of the SINAI-DL team at RumourEval (Task 7 in SemEval 2019, subtask A: SDQC). SDQC addresses the challenge of rumour stance classification as an indirect way of identifying potential rumours. Given a tweet with several replies, our system classifies each reply into either supporting, denying, questioning or commenting on the underlying rumours. We have applied data augmentation, temporal expressions labelling and transfer learning with a four-layer neural classifier. We achieve an accuracy of 0.715 with the official run over reply tweets.

2017

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SINAI at SemEval-2017 Task 4: User based classification
Salud María Jiménez-Zafra | Arturo Montejo-Ráez | Maite Martin | L. Alfonso Ureña-López
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This document describes our participation in SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have only reported results for subtask B - English, determining the polarity towards a topic on a two point scale (positive or negative sentiment). Our main contribution is the integration of user information in the classification process. A SVM model is trained with Word2Vec vectors from user’s tweets extracted from his timeline. The obtained results show that user-specific classifiers trained on tweets from user timeline can introduce noise as they are error prone because they are classified by an imperfect system. This encourages us to explore further integration of user information for author-based Sentiment Analysis.

2016

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Pictogrammar: an AAC device based on a semantic grammar
Fernando Martínez-Santiago | Miguel Ángel García-Cumbreras | Arturo Montejo-Ráez | Manuel Carlos Díaz-Galiano
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications

2013

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SINAI: Machine Learning and Emotion of the Crowd for Sentiment Analysis in Microblogs
Eugenio Martínez-Cámara | Arturo Montejo-Ráez | M. Teresa Martín-Valdivia | L. Alfonso Ureña-López
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2012

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Random Walk Weighting over SentiWordNet for Sentiment Polarity Detection on Twitter
Arturo Montejo-Ráez | Eugenio Martínez-Cámara | M. Teresa Martín-Valdivia | L. Alfonso Ureña-López
Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis

2007

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Combining Lexical-Syntactic Information with Machine Learning for Recognizing Textual Entailment
Arturo Montejo-Ráez | Jose Manuel Perea | Fernando Martínez-Santiago | Miguel Ángel García-Cumbreras | Maite Martín-Valdivia | Alfonso Ureña-López
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing