Miguel B. Almeida

Also published as: Miguel Almeida


2014

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TUGAS: Exploiting unlabelled data for Twitter sentiment analysis
Silvio Amir | Miguel B. Almeida | Bruno Martins | João Filgueiras | Mário J. Silva
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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A Joint Model for Quotation Attribution and Coreference Resolution
Mariana S. C. Almeida | Miguel B. Almeida | André F. T. Martins
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Priberam Compressive Summarization Corpus: A New Multi-Document Summarization Corpus for European Portuguese
Miguel B. Almeida | Mariana S. C. Almeida | André F. T. Martins | Helena Figueira | Pedro Mendes | Cláudia Pinto
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper, we introduce the Priberam Compressive Summarization Corpus, a new multi-document summarization corpus for European Portuguese. The corpus follows the format of the summarization corpora for English in recent DUC and TAC conferences. It contains 80 manually chosen topics referring to events occurred between 2010 and 2013. Each topic contains 10 news stories from major Portuguese newspapers, radio and TV stations, along with two human generated summaries up to 100 words. Apart from the language, one important difference from the DUC/TAC setup is that the human summaries in our corpus are \emph{compressive}: the annotators performed only sentence and word deletion operations, as opposed to generating summaries from scratch. We use this corpus to train and evaluate learning-based extractive and compressive summarization systems, providing an empirical comparison between these two approaches. The corpus is made freely available in order to facilitate research on automatic summarization.

2013

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Fast and Robust Compressive Summarization with Dual Decomposition and Multi-Task Learning
Miguel Almeida | André Martins
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Turning on the Turbo: Fast Third-Order Non-Projective Turbo Parsers
André Martins | Miguel Almeida | Noah A. Smith
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)