2020
Authors
Poinhos, R; Oliveira, BMPM; Sorokina, A; Franchini, B; Afonso, C; de Almeida, MDV;
Publication
PROCEEDINGS OF THE NUTRITION SOCIETY
Abstract
2020
Authors
Lopes, MS; Oliveira, BMPM; Neves, O; Melim, D; Freitas, P; Correia, F;
Publication
PROCEEDINGS OF THE NUTRITION SOCIETY
Abstract
2020
Authors
Are, M; Santos, E; Oliveira, BMPM; Correia, F; Poínhos, R;
Publication
PROCEEDINGS OF THE NUTRITION SOCIETY
Abstract
2020
Authors
Cardoso, F; Azevedo, M; Oliveira, B; Poinhos, R; Carvaho, J; Almeida, R; Correia, F;
Publication
PROCEEDINGS OF THE NUTRITION SOCIETY
Abstract
2020
Authors
Gandhi, S; Mansouri, B; Campos, R; Jatowt, A;
Publication
WWW'20: COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2020
Abstract
Users tend to search over the Internet to get the most updated news when an event occurs. Search engines should then be capable of effectively retrieving relevant documents for event-related queries. As the previous studies have shown, different retrieval models are needed for different types of events. Therefore, the first step for improving effectiveness is identifying the event-related queries and determining their types. In this paper, we propose a novel model based on deep neural networks to classify event-related queries into four categories: periodic, aperiodic, one-time-only, and non-event. The proposed model combines recurrent neural networks (by feeding two LSTM layers with query frequencies) and visual recognition models (by transforming time-series data from a 1D signal to a 2D image - later passed to a CNN model) for effective query type estimation. Worth noting is that our method uses only the time-series data of query frequencies, without the need to resort to any external sources such as contextual data, which makes it language and domain-independent with regards to the query issued. For evaluation, we build upon the previous datasets on event-related queries to create a new dataset that fits the purpose of our experiments. The obtained results show that our proposed model can achieve an F1-score of 0.87.
2020
Authors
Balali, A; Asadpour, M; Campos, R; Jatowt, A;
Publication
KNOWLEDGE-BASED SYSTEMS
Abstract
Event extraction (EE) is one of the core information extraction tasks, whose purpose is to automatically identify and extract information about incidents and their actors from texts. This may be beneficial to several domains such as knowledge base construction, question answering and summarization tasks, to name a few. The problem of extracting event information from texts is longstanding and usually relies on elaborately designed lexical and syntactic features, which, however, take a large amount of human effort and lack generalization. More recently, deep neural network approaches have been adopted as a means to learn underlying features automatically. However, existing networks do not make full use of syntactic features, which play a fundamental role in capturing very long-range dependencies. Also, most approaches extract each argument of an event separately without considering associations between arguments which ultimately leads to low efficiency, especially in sentences with multiple events. To address the above-referred problems, we propose a novel joint event extraction framework that aims to extract multiple event triggers and arguments simultaneously by introducing shortest dependency path in the dependency graph. We do this by eliminating irrelevant words in the sentence, thus capturing long-range dependencies. Also, an attention-based graph convolutional network is proposed, to carry syntactically related information along the shortest paths between argument candidates that captures and aggregates the latent associations between arguments; a problem that has been overlooked by most of the literature. Our results show a substantial improvement over state-of-the-art methods on two datasets, namely ACE 2005 and TAC KBP 2015.
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