Mariana S. C. Almeida


2017

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The SUMMA Platform Prototype
Renars Liepins | Ulrich Germann | Guntis Barzdins | Alexandra Birch | Steve Renals | Susanne Weber | Peggy van der Kreeft | Hervé Bourlard | João Prieto | Ondřej Klejch | Peter Bell | Alexandros Lazaridis | Alfonso Mendes | Sebastian Riedel | Mariana S. C. Almeida | Pedro Balage | Shay B. Cohen | Tomasz Dwojak | Philip N. Garner | Andreas Giefer | Marcin Junczys-Dowmunt | Hina Imran | David Nogueira | Ahmed Ali | Sebastião Miranda | Andrei Popescu-Belis | Lesly Miculicich Werlen | Nikos Papasarantopoulos | Abiola Obamuyide | Clive Jones | Fahim Dalvi | Andreas Vlachos | Yang Wang | Sibo Tong | Rico Sennrich | Nikolaos Pappas | Shashi Narayan | Marco Damonte | Nadir Durrani | Sameer Khurana | Ahmed Abdelali | Hassan Sajjad | Stephan Vogel | David Sheppey | Chris Hernon | Jeff Mitchell
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.

2016

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Jointly Learning to Embed and Predict with Multiple Languages
Daniel C. Ferreira | André F. T. Martins | Mariana S. C. Almeida
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Aligning Opinions: Cross-Lingual Opinion Mining with Dependencies
Mariana S. C. Almeida | Cláudia Pinto | Helena Figueira | Pedro Mendes | André F. T. Martins
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Lisbon: Evaluating TurboSemanticParser on Multiple Languages and Out-of-Domain Data
Mariana S. C. Almeida | André F. T. Martins
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2014

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Priberam: A Turbo Semantic Parser with Second Order Features
André F. T. Martins | Mariana S. C. Almeida
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.