scholarly journals Semantic frame induction through the detection of communities of verbs and their arguments

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Eugénio Ribeiro ◽  
Andreia Sofia Teixeira ◽  
Ricardo Ribeiro ◽  
David Martins de Matos
Keyword(s):  
Community Detection ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Graph Clustering ◽  
Semantic Role ◽  
Detection Problem ◽  
Current State ◽  
Textual Data ◽  
Semantic Frames ◽  
Do So

Abstract Resources such as FrameNet, which provide sets of semantic frame definitions and annotated textual data that maps into the evoked frames, are important for several NLP tasks. However, they are expensive to build and, consequently, are unavailable for many languages and domains. Thus, approaches able to induce semantic frames in an unsupervised manner are highly valuable. In this paper we approach that task from a network perspective as a community detection problem that targets the identification of groups of verb instances that evoke the same semantic frame and verb arguments that play the same semantic role. To do so, we apply a graph-clustering algorithm to a graph with contextualized representations of verb instances or arguments as nodes connected by edges if the distance between them is below a threshold that defines the granularity of the induced frames. By applying this approach to the benchmark dataset defined in the context of SemEval 2019, we outperformed all of the previous approaches to the task, achieving the current state-of-the-art performance.

scholarly journals Context-aware Frame-Semantic Role Labeling

2015 ◽  
Vol 3 ◽  
pp. 449-460 ◽  
Author(s):  
Michael Roth ◽  
Mirella Lapata
Keyword(s):  
Social Network ◽  
Question Answering ◽  
State Of The Art ◽  
Semantic Role ◽  
Context Aware ◽  
Semantic Role Labeling ◽  
Current State ◽  
Sentence Level ◽  
Small Set ◽  
Labeling System

Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging task and corresponding models are typically only trained on a small set of sentence-level annotations. In this paper, we present a semantic role labeling system that takes into account sentence and discourse context. We introduce several new features which we motivate based on linguistic insights and experimentally demonstrate that they lead to significant improvements over the current state-of-the-art in FrameNet-based semantic role labeling.

scholarly journals Assessing Sustainability in the Shipbuilding Supply Chain 4.0: A Systematic Review

2020 ◽  
Vol 12 (16) ◽  
pp. 6373 ◽  
Author(s):  
Magdalena Ramirez-Peña ◽  
Francisco J. Abad Fraga ◽  
Jorge Salguero ◽  
Moises Batista
Keyword(s):  
Systematic Review ◽  
Supply Chain ◽  
Scientific Community ◽  
State Of The Art ◽  
Current State ◽  
Enabling Technologies ◽  
Global Vision ◽  
The Subject ◽  
Key Enabling Technologies ◽  
Do So

The supply chain is currently taking on a very important role in organizations seeking to improve the competitiveness and profitability of the company. Its transversal character mainly places it in an unbeatable position to achieve this role. This article, through a study of each of the key enabling technologies of Industry 4.0, aims to obtain a general overview of the current state of the art in shipbuilding adapted to these technologies. To do so, a systematic review of what the scientific community says is carried out, dividing each of the technologies into different categories. In addition, the global vision of countries interested in each of the enabling technologies is also studied. Both studies present a general vision to the companies of the concerns of the scientific community, thus encouraging research on the subject that is focused on the sustainability of the shipbuilding supply chain.

The Present State of Autonomous Underwater Vehicle (AUV) Applications and Technologies

2008 ◽  
Vol 42 (1) ◽  
pp. 44-51 ◽  
Author(s):  
J. W. Nicholson ◽  
A. J. Healey
Keyword(s):  
Present State ◽  
Autonomous Underwater Vehicle ◽  
State Of The Art ◽  
Underwater Vehicle ◽  
The State ◽  
Design Features ◽  
Current State ◽  
Do So

AUVs have proved their usefulness in recent years and continue to do so. This paper is a review of the current state of the art of AUVs. Present AUV capabilities are reviewed through a discussion of feasible present-day AUV missions. The state of key AUV design features and sensor technologies is also addressed, identifying those areas most critical to continued future progress in AUV development.

scholarly journals Gamificação e realidade aumentada em processos de ensino-aprendizagem: uma revisão sistemática da literatura

2021 ◽  
Vol 14 (2) ◽  
pp. 262-274
Author(s):  
Orlando Rosa Junior ◽  
Tiago De Oliveira ◽  
Ezequiel Zorzal
Keyword(s):  
Augmented Reality ◽  
State Of The Art ◽  
Learning Assessment ◽  
Assessment Methodology ◽  
Know How ◽  
Current State ◽  
Research Questions ◽  
Programming Knowledge ◽  
Specific Learning ◽  
Do So

This paper presents a Systematic Literature Review (RSL) on the use of Gamification and Augmented Reality applied in Education. RSL enabled mapping and knowledge of the current state of related studies. Thirty articles related to the state of the art were analyzed. From the analysis, it was found that there is no specific learning assessment methodology when applying the Augmented Reality and Gamification tools. An analysis of the results was made to answer the research questions. The study showed that lack of programming knowledge is also a factor that hinders the advancement of research since the researcher must know how to develop the application or have in his team someone who has the competence to do so. Finally, we present the comparatives and analyzes of the studies to obtain answers based on the research questions developed.

scholarly journals Local clustering via approximate heat kernel PageRank with subgraph sampling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhenqi Lu ◽  
Johan Wahlström ◽  
Arye Nehorai
Keyword(s):  
Heat Kernel ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Graph Clustering ◽  
Approximate Algorithms ◽  
Local Cluster ◽  
Massive Graphs ◽  
Approximation Errors ◽  
Local Clustering ◽  
Large Systems

AbstractGraph clustering, a fundamental technique in network science for understanding structures in complex systems, presents inherent problems. Though studied extensively in the literature, graph clustering in large systems remains particularly challenging because massive graphs incur a prohibitively large computational load. The heat kernel PageRank provides a quantitative ranking of nodes, and a local cluster can be efficiently found by performing a sweep over the heat kernel PageRank vector. But computing an exact heat kernel PageRank vector may be expensive, and approximate algorithms are often used instead. Most approximate algorithms compute the heat kernel PageRank vector on the whole graph, and thus are dependent on global structures. In this paper, we present an algorithm for approximating the heat kernel PageRank on a local subgraph. Moreover, we show that the number of computations required by the proposed algorithm is sublinear in terms of the expected size of the local cluster of interest, and that it provides a good approximation of the heat kernel PageRank, with approximation errors bounded by a probabilistic guarantee. Numerical experiments verify that the local clustering algorithm using our approximate heat kernel PageRank achieves state-of-the-art performance.

Single-ion mass spectrometry at 100 ppt and beyond

2002 ◽  
Vol 80 (11) ◽  
pp. 1329-1336 ◽  
Author(s):  
S Rainville ◽  
J K Thompson ◽  
D E Pritchard
Keyword(s):  
Mass Spectrometry ◽  
Penning Trap ◽  
State Of The Art ◽  
Normal Modes ◽  
Mass Ratio ◽  
Current State ◽  
Order Of Magnitude ◽  
And Control ◽  
Source Of Error ◽  
Do So

Using a Penning trap single-ion mass spectrometer, we measured the atomic masses of 14 isotopes with a fractional accuracy of ~10–10. The precision on these measurements was limited by the temporal fluctuations of our magnetic field. By trapping two different ions in the same Penning trap at the same time, we have recently been able to virtually eliminate that source of error. We can now simultaneously measure the ratio of the two ion's cyclotron frequencies (from which we obtain their atomic mass ratio) with a precision of about 10–11 in only a few hours. To perform these comparisons, we must be able to measure and control all three normal modes of motion of each ion — cyclotron, axial, and magnetron — and have developed novel techniques to do so. This new technique shows promise of expanding the precision of mass spectrometry by an order of magnitude beyond the current state-of-the-art. PACS Nos.: 32.10Bi, 06.20Jr, 06.30Dr, 07.75+h, 07.77-n

scholarly journals A Graph Clustering Algorithm Using Attraction-Force Similarity for Community Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 13683-13692 ◽  
Author(s):  
Hongfang Zhou ◽  
Bingyan Xi ◽  
Yihui Zhang ◽  
Junhuai Li ◽  
Facun Zhang
Keyword(s):  
Community Detection ◽  
Clustering Algorithm ◽  
Graph Clustering ◽  
Attraction Force

scholarly journals Automatic Curriculum Learning For Deep RL: A Short Survey

2020 ◽  
Author(s):  
Rémy Portelas ◽  
Cédric Colas ◽  
Lilian Weng ◽  
Katja Hofmann ◽  
Pierre-Yves Oudeyer
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Learning Problems ◽  
Learning Trajectories ◽  
Asymptotic Performance ◽  
Cross Breeding ◽  
Current State ◽  
New Ideas ◽  
Multi Agent ◽  
Do So

Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in Deep Reinforcement Learning (DRL). These methods shape the learning trajectories of agents by challenging them with tasks adapted to their capacities. In recent years, they have been used to improve sample efficiency and asymptotic performance, to organize exploration, to encourage generalization or to solve sparse reward problems, among others. To do so, ACL mechanisms can act on many aspects of learning problems. They can optimize domain randomization for Sim2Real transfer, organize task presentations in multi-task robotic settings, order sequences of opponents in multi-agent scenarios, etc. The ambition of this work is dual: 1) to present a compact and accessible introduction to the Automatic Curriculum Learning literature and 2) to draw a bigger picture of the current state of the art in ACL to encourage the cross-breeding of existing concepts and the emergence of new ideas.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

Schizophrenia Research: Current State of the Art

1976 ◽  
Vol 21 (7) ◽  
pp. 497-498
Author(s):  
STANLEY GRAND
Keyword(s):  
State Of The Art ◽  
Current State
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