INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Field-programmable gate array & Control theory. The organization has 932 authors who have published 2618 publications receiving 37658 citations.

Using morphossyntactic information in TTS systems: comparing strategies for European Portuguese

Abstract: To improve the quality of the speech produced by a Text-to-Speech (TTS) system, it is important to obtain the maximum amount of information from the input text that may help in this task. This covers a wide range of possibilities that can go from the simple conversion of non orthographic items to more complex syntactic and semantic analysis. In this paper, we present the development of a morphossyntactic tagging system and analyze its influence on the performance of a TTS system for European Portuguese.

The PASCAL Challenge on Grammar Induction

Abstract: This paper presents the results of the PASCAL Challenge on Grammar Induction, a competition in which competitors sought to predict part-of-speech and dependency syntax from text. Although many previous competitions have featured dependency grammars or parts-of-speech, these were invariably framed as supervised learning and/or domain adaption. This is the first challenge to evaluate unsupervised induction systems, a sub-field of syntax which is rapidly becoming very popular. Our challenge made use of a 10 different treebanks annotated in a range of different linguistic formalisms and covering 9 languages. We provide an overview of the approaches taken by the participants, and evaluate their results on each dataset using a range of different evaluation metrics.

A parallel algorithm for the extraction of structured motifs

Abstract: In this work we propose a parallel algorithm for the efficient extraction of binding-site consensus from genomic sequences. This algorithm, based on an existing approach, extracts structured motifs, that consist of an ordered collection of p ≥ 1 boxes with sizes and spacings between them specified by given parameters. The contents of the boxes, which represent the extracted motifs, are unknown at the start of the process and are found by the algorithm using a suffix tree as the fundamental data structure. By partitioning the structured motif searching space we divide the most demanding part of the algorithm by a number of processors that can be loosely coupled. In this way we obtain, under conditions that are easily met, a speedup that is linear on the number of available processing units. This speedup is verified by both theoretical and experimental analysis, also presented in this paper.

The epidemiology ontology: an ontology for the semantic annotation of epidemiological resources

Abstract: Epidemiology is a data-intensive and multi-disciplinary subject, where data integration, curation and sharing are becoming increasingly relevant, given its global context and time constraints. The semantic annotation of epidemiology resources is a cornerstone to effectively support such activities. Although several ontologies cover some of the subdomains of epidemiology, we identified a lack of semantic resources for epidemiology-specific terms. This paper addresses this need by proposing the Epidemiology Ontology (EPO) and by describing its integration with other related ontologies into a semantic enabled platform for sharing epidemiology resources. The EPO follows the OBO Foundry guidelines and uses the Basic Formal Ontology (BFO) as an upper ontology. The first version of EPO models several epidemiology and demography parameters as well as transmission of infection processes, participants and related procedures. It currently has nearly 200 classes and is designed to support the semantic annotation of epidemiology resources and data integration, as well as information retrieval and knowledge discovery activities. EPO is under active development and is freely available at https://code.google.com/p/epidemiology-ontology/ . We believe that the annotation of epidemiology resources with EPO will help researchers to gain a better understanding of global epidemiological events by enhancing data integration and sharing.

Towards Generalization in QBF Solving via Machine Learning.

Abstract: There are well known cases of Quantified Boolean Formulas (QBFs) that have short winning strategies (Skolem/Herbrand functions) but that are hard to solve by nowadays solvers. This paper argues that a solver benefits from generalizing a set of individual wins into a strategy. This idea is realized on top of the competitive RAReQS algorithm by utilizing machine learning, which enables learning shorter strategies. The implemented prototype QFUN has won the first place in the non-CNF track of the most recent QBF competition.