The current deep learning works on metaphor detection have only considered this task independently, ignoring the useful knowledge from the related tasks and knowledge resources. In this work, we introduce two novel mechanisms to improve the performance of the deep learning models for metaphor detection. The first mechanism employs graph convolutional neural networks (GCN) with dependency parse trees to directly connect the words of interest with their important context words for metaphor detection. The GCN networks in this work also present a novel control mechanism to filter the learned representation vectors to retain the most important information for metaphor detection. The second mechanism, on the other hand, features a multi-task learning framework that exploits the similarity between word sense disambiguation and metaphor detection to transfer the knowledge between the two tasks. The extensive experiments demonstrate the effectiveness of the proposed techniques, yielding the state-of-the-art performance over several datasets.
Most of the previous work on Event Detection (ED) has only considered the datasets with a small number of event types (i.e., up to 38 types). In this work, we present the first study on fine-grained ED (FED) where the evaluation dataset involves much more fine-grained event types (i.e., 449 types). We propose a novel method to transform the Semcor dataset for Word Sense Disambiguation into a large and high-quality dataset for FED. Extensive evaluation of the current ED methods is conducted to demonstrate the challenges of the generated datasets for FED, calling for more research effort in this area.
The goal of Event Factuality Prediction (EFP) is to determine the factual degree of an event mention, representing how likely the event mention has happened in text. Current deep learning models has demonstrated the importance of syntactic and semantic structures of the sentences to identify important context words for EFP. However, the major problem with these EFP models is that they only encode the one-hop paths between the words (i.e., the direct connections) to form the sentence structures. In this work, we show that the multihop paths between the words are also necessary to compute the sentence structures for EFP. To this end, we introduce a novel deep learning model for EFP that explicitly considers multi-hop paths with both syntax-based and semantic-based edges between the words to obtain sentence structures for representation learning in EFP. We demonstrate the effectiveness of the proposed model via the extensive experiments in this work.
An implementation of a Smart meter which has a feature of the advantages of IEEE1888 and 6LoWPAN, the integration of these protocols into Smart meter is of important significance in fulfilling its function as a Gateway in Building Energy Management Systems. The aim of this paper is to show the implementation of a smart meter which also functions as an IEEE1888-6LoWPAN gateway. In this system, Smart Meter/Gateway is responsible for sensing the consumption as well as relaying the data from 6LoWPAN sensors in building blocks to an anywhere IEEE1888 Storage. Meter, IEEE1888, 6LoWPAN, Gateway.
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