Sebastião Miranda


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Hierarchical Nested Named Entity Recognition
Zita Marinho | Afonso Mendes | Sebastião Miranda | David Nogueira
Proceedings of the 2nd Clinical Natural Language Processing Workshop

In the medical domain and other scientific areas, it is often important to recognize different levels of hierarchy in mentions, such as those related to specific symptoms or diseases associated with different anatomical regions. Unlike previous approaches, we build a transition-based parser that explicitly models an arbitrary number of hierarchical and nested mentions, and propose a loss that encourages correct predictions of higher-level mentions. We further introduce a set of modifier classes which introduces certain concepts that change the meaning of an entity, such as absence, or uncertainty about a given disease. Our proposed model achieves state-of-the-art results in medical entity recognition datasets, using both nested and hierarchical mentions.

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Jointly Extracting and Compressing Documents with Summary State Representations
Afonso Mendes | Shashi Narayan | Sebastião Miranda | Zita Marinho | André F. T. Martins | Shay B. Cohen
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We present a new neural model for text summarization that first extracts sentences from a document and then compresses them. The pro-posed model offers a balance that sidesteps thedifficulties in abstractive methods while gener-ating more concise summaries than extractivemethods. In addition, our model dynamically determines the length of the output summary based on the gold summaries it observes during training and does not require length constraints typical to extractive summarization. The model achieves state-of-the-art results on the CNN/DailyMail and Newsroom datasets, improving over current extractive and abstractive methods. Human evaluations demonstratethat our model generates concise and informa-tive summaries. We also make available a new dataset of oracle compressive summaries derived automatically from the CNN/DailyMailreference summaries.


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The SUMMA Platform: A Scalable Infrastructure for Multi-lingual Multi-media Monitoring
Ulrich Germann | Renārs Liepins | Guntis Barzdins | Didzis Gosko | Sebastião Miranda | David Nogueira
Proceedings of ACL 2018, System Demonstrations

The open-source SUMMA Platform is a highly scalable distributed architecture for monitoring a large number of media broadcasts in parallel, with a lag behind actual broadcast time of at most a few minutes. The Platform offers a fully automated media ingestion pipeline capable of recording live broadcasts, detection and transcription of spoken content, translation of all text (original or transcribed) into English, recognition and linking of Named Entities, topic detection, clustering and cross-lingual multi-document summarization of related media items, and last but not least, extraction and storage of factual claims in these news items. Browser-based graphical user interfaces provide humans with aggregated information as well as structured access to individual news items stored in the Platform’s database. This paper describes the intended use cases and provides an overview over the system’s implementation.

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Multilingual Clustering of Streaming News
Sebastião Miranda | Artūrs Znotiņš | Shay B. Cohen | Guntis Barzdins
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Clustering news across languages enables efficient media monitoring by aggregating articles from multilingual sources into coherent stories. Doing so in an online setting allows scalable processing of massive news streams. To this end, we describe a novel method for clustering an incoming stream of multilingual documents into monolingual and crosslingual clusters. Unlike typical clustering approaches that report results on datasets with a small and known number of labels, we tackle the problem of discovering an ever growing number of cluster labels in an online fashion, using real news datasets in multiple languages. In our formulation, the monolingual clusters group together documents while the crosslingual clusters group together monolingual clusters, one per language that appears in the stream. Our method is simple to implement, computationally efficient and produces state-of-the-art results on datasets in German, English and Spanish.


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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.