João P. Neto

Also published as: Joao Neto, Joao P. Neto, João Neto, João Paulo Neto


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Automatic Keyword Extraction on Twitter
Luís Marujo | Wang Ling | Isabel Trancoso | Chris Dyer | Alan W. Black | Anatole Gershman | David Martins de Matos | João Neto | Jaime Carbonell
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Extending a Single-Document Summarizer to Multi-Document: a Hierarchical Approach
Luís Marujo | Ricardo Ribeiro | David Martins de Matos | João Neto | Anatole Gershman | Jaime Carbonell
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics


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SAVAS: Collecting, Annotating and Sharing Audiovisual Language Resources for Automatic Subtitling
Arantza del Pozo | Carlo Aliprandi | Aitor Álvarez | Carlos Mendes | Joao P. Neto | Sérgio Paulo | Nicola Piccinini | Matteo Raffaelli
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper describes the data collection, annotation and sharing activities carried out within the FP7 EU-funded SAVAS project. The project aims to collect, share and reuse audiovisual language resources from broadcasters and subtitling companies to develop large vocabulary continuous speech recognisers in specific domains and new languages, with the purpose of solving the automated subtitling needs of the media industry.


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Recognition of Named-Event Passages in News Articles
Luis Marujo | Wang Ling | Anatole Gershman | Jaime Carbonell | João P. Neto | David Matos
Proceedings of COLING 2012: Demonstration Papers

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Supervised Topical Key Phrase Extraction of News Stories using Crowdsourcing, Light Filtering and Co-reference Normalization
Luís Marujo | Anatole Gershman | Jaime Carbonell | Robert Frederking | João P. Neto
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Fast and effective automated indexing is critical for search and personalized services. Key phrases that consist of one or more words and represent the main concepts of the document are often used for the purpose of indexing. In this paper, we investigate the use of additional semantic features and pre-processing steps to improve automatic key phrase extraction. These features include the use of signal words and freebase categories. Some of these features lead to significant improvements in the accuracy of the results. We also experimented with 2 forms of document pre-processing that we call light filtering and co-reference normalization. Light filtering removes sentences from the document, which are judged peripheral to its main content. Co-reference normalization unifies several written forms of the same named entity into a unique form. We also needed a “Gold Standard” ― a set of labeled documents for training and evaluation. While the subjective nature of key phrase selection precludes a true “Gold Standard”, we used Amazon's Mechanical Turk service to obtain a useful approximation. Our data indicates that the biggest improvements in performance were due to shallow semantic features, news categories, and rhetorical signals (nDCG 78.47% vs. 68.93%). The inclusion of deeper semantic features such as Freebase sub-categories was not beneficial by itself, but in combination with pre-processing, did cause slight improvements in the nDCG scores.


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Reengineering a Domain-Independent Framework for Spoken Dialogue Systems
Filipe M. Martins | Ana Mendes | Mácio Freitas Viveiros | Joana Paulo Pardal | Pedro Arez | Nuno J. Mamede | João Paulo Neto
Software Engineering, Testing, and Quality Assurance for Natural Language Processing


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The COST278 Pan-European Broadcast News Database
An Vandecatseye | Jean-Pierre Martens | Joao Neto | Hugo Meinedo | Carmen Garcia-Mateo | Javier Dieguez | France Mihelic | Janez Zibert | Jan Nouza | Petr David | Matus Pleva | Anton Cizmar | Harris Papageorgiou | Christina Alexandris
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)