Pedro Bispo Santos


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A domain-agnostic approach for opinion prediction on speech
Pedro Bispo Santos | Lisa Beinborn | Iryna Gurevych
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)

We explore a domain-agnostic approach for analyzing speech with the goal of opinion prediction. We represent the speech signal by mel-frequency cepstral coefficients and apply long short-term memory neural networks to automatically learn temporal regularities in speech. In contrast to previous work, our approach does not require complex feature engineering and works without textual transcripts. As a consequence, it can easily be applied on various speech analysis tasks for different languages and the results show that it can nevertheless be competitive to the state-of-the-art in opinion prediction. In a detailed error analysis for opinion mining we find that our approach performs well in identifying speaker-specific characteristics, but should be combined with additional information if subtle differences in the linguistic content need to be identified.


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Counting What Counts: Decompounding for Keyphrase Extraction
Nicolai Erbs | Pedro Bispo Santos | Torsten Zesch | Iryna Gurevych
Proceedings of the ACL 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction


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DKPro Keyphrases: Flexible and Reusable Keyphrase Extraction Experiments
Nicolai Erbs | Pedro Bispo Santos | Iryna Gurevych | Torsten Zesch
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations