scholarly journals Impresso Inspect and Compare. Visual Comparison of Semantically Enriched Historical Newspaper Articles

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 348 ◽  
Author(s):  
Marten Düring ◽  
Roman Kalyakin ◽  
Estelle Bunout ◽  
Daniele Guido
Keyword(s):  
Text Mining ◽  
Interface Design ◽  
State Of The Art ◽  
Critical Assessment ◽  
Data Driven ◽  
Distant Reading ◽  
Named Entities ◽  
Document Collections ◽  
Search Queries ◽  
Current State

The automated enrichment of mass-digitised document collections using techniques such as text mining is becoming increasingly popular. Enriched collections offer new opportunities for interface design to allow data-driven and visualisation-based search, exploration and interpretation. Most such interfaces integrate close and distant reading and represent semantic, spatial, social or temporal relations, but often lack contrastive views. Inspect and Compare (I&C) contributes to the current state of the art in interface design for historical newspapers with highly versatile side-by-side comparisons of query results and curated article sets based on metadata and semantic enrichments. I&C takes search queries and pre-curated article sets as inputs and allows comparisons based on the distributions of newspaper titles, publication dates and automatically generated enrichments, such as language, article types, topics and named entities. Contrastive views of such data reveal patterns, help humanities scholars to improve search strategies and to facilitate a critical assessment of the overall data quality. I&C is part of the impresso interface for the exploration of digitised and semantically enriched historical newspapers.

scholarly journals Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

2018 ◽  
Vol 61 ◽  
pp. 65-170 ◽  
Author(s):  
Albert Gatt ◽  
Emiel Krahmer
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Natural Language Generation ◽  
Data Driven ◽  
Research Topics ◽  
Language Generation ◽  
The Past ◽  
Current State ◽  
Linguistic Input ◽  
New Applications

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past two decades, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of NLP, with an emphasis on different evaluation methods and the relationships between them.

scholarly journals Identification of Causal Effects in the Presence of Selection Bias

2019 ◽  
Vol 33 ◽  
pp. 2744-2751 ◽  
Author(s):  
Juan D. Correa ◽  
Jin Tian ◽  
Elias Bareinboim
Keyword(s):  
State Of The Art ◽  
Causal Relation ◽  
Causal Effects ◽  
Data Driven ◽  
Systematic Bias ◽  
Types Of Knowledge ◽  
External Data ◽  
Current State ◽  
Back Door ◽  
Common Barriers

Cause-and-effect relations are one of the most valuable types of knowledge sought after throughout the data-driven sciences since they translate into stable and generalizable explanations as well as efficient and robust decision-making capabilities. Inferring these relations from data, however, is a challenging task. Two of the most common barriers to this goal are known as confounding and selection biases. The former stems from the systematic bias introduced during the treatment assignment, while the latter comes from the systematic bias during the collection of units into the sample. In this paper, we consider the problem of identifiability of causal effects when both confounding and selection biases are simultaneously present. We first investigate the problem of identifiability when all the available data is biased. We prove that the algorithm proposed by [Bareinboim and Tian, 2015] is, in fact, complete, namely, whenever the algorithm returns a failure condition, no identifiability claim about the causal relation can be made by any other method. We then generalize this setting to when, in addition to the biased data, another piece of external data is available, without bias. It may be the case that a subset of the covariates could be measured without bias (e.g., from census). We examine the problem of identifiability when a combination of biased and unbiased data is available. We propose a new algorithm that subsumes the current state-of-the-art method based on the back-door criterion.

scholarly journals Learning from Easy to Complex: Adaptive Multi-Curricula Learning for Neural Dialogue Generation

2020 ◽  
Vol 34 (05) ◽  
pp. 7472-7479
Author(s):  
Hengyi Cai ◽  
Hongshen Chen ◽  
Cheng Zhang ◽  
Yonghao Song ◽  
Xiaofang Zhao ◽  
...  
Keyword(s):  
State Of The Art ◽  
Data Driven ◽  
Generation Model ◽  
Dialogue Systems ◽  
Multiple Perspectives ◽  
Learning Efficiency ◽  
Learning Framework ◽  
Current State ◽  
Efficiency And Effectiveness ◽  
Complex Adaptive

Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neural dialogue generation models. What is more, so far, there are no unified dialogue complexity measurements, and the dialogue complexity embodies multiple aspects of attributes—specificity, repetitiveness, relevance, etc. Inspired by human behaviors of learning to converse, where children learn from easy dialogues to complex ones and dynamically adjust their learning progress, in this paper, we first analyze five dialogue attributes to measure the dialogue complexity in multiple perspectives on three publicly available corpora. Then, we propose an adaptive multi-curricula learning framework to schedule a committee of the organized curricula. The framework is established upon the reinforcement learning paradigm, which automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model. Extensive experiments conducted on five state-of-the-art models demonstrate its learning efficiency and effectiveness with respect to 13 automatic evaluation metrics and human judgments.

scholarly journals Mediated by Code: Unpacking Algorithmic Curation of Urban Experiences

2021 ◽  
Vol 9 (4) ◽  
pp. 250-259 ◽  
Author(s):  
Annelien Smets ◽  
Pieter Ballon ◽  
Nils Walravens
Keyword(s):  
State Of The Art ◽  
Communication Technologies ◽  
Urban Life ◽  
Data Driven ◽  
Information Flows ◽  
Life Data ◽  
Current State ◽  
Online Platforms ◽  
The City ◽  
Timely Topic

Amid the widespread diffusion of digital communication technologies, our cities are at a critical juncture as these technologies are entering all aspects of urban life. Data-driven technologies help citizens to navigate the city, find friends, or discover new places. While these technology-mediated activities come in scope of scholarly research, we lack an understanding of the underlying curation mechanisms that select and present the particular information citizens are exposed to. Nevertheless, such an understanding is crucial to deal with the risk of the socio-cultural polarization assumedly reinforced by this kind of algorithmic curation. Drawing upon the vast amount of work on algorithmic curation in online platforms, we construct an analytical lens that is applied to the urban environment to establish an understanding of algorithmic curation of urban experiences. In this way, this article demonstrates that cities could be considered as a new materiality of curational platforms. Our framework outlines the various urban information flows, curation logics, and stakeholders involved. This work contributes to the current state of the art by bridging the gap between online and offline algorithmic curation and by providing a novel conceptual framework to study this timely topic.

scholarly journals AI EDAM special issue: advances in implemented shape grammars: solutions and applications

2018 ◽  
Vol 32 (2) ◽  
pp. 131-137 ◽  
Author(s):  
Sara Eloy ◽  
Pieter Pauwels ◽  
Athanassios Economou
Keyword(s):  
Interface Design ◽  
State Of The Art ◽  
Real Life ◽  
Shape Grammars ◽  
Special Issue ◽  
Technical Design ◽  
Current State ◽  
The Future ◽  
Current Special Issue ◽  
Life Design

AbstractThis paper introduces the special issue “Advances in Implemented Shape Grammars: Solutions and Applications” and frames the topic of computer implementations of shape grammars, both with a theoretical and an applied focus. This special issue focuses on the current state of the art regarding computer implementations of shape grammars and brings a discussion about how those systems can evolve in the coming years so that they can be used in real life design scenarios. This paper presents a brief state of the art of shape grammars implementation and an overview of the papers included in the current special issue categorized under technical design, interpreters and interface design, and uses cases. The paper ends with a comprehensive outlook into the future of shape grammars implementations.

scholarly journals Research Paper on Data-Driven Business Models: Why the Asia-Pacific Region Serves as an Example for the Western Companies? How Might they Improve Their Business Models in 5 Years?

2020 ◽  
Vol 11 (6) ◽  
pp. 169-176
Author(s):  
Tsvetelin Anastasov ◽  
Keyword(s):  
Business Models ◽  
State Of The Art ◽  
Data Driven ◽  
Asia Pacific ◽  
Pacific Region ◽  
Clear Definition ◽  
Asia Pacific Region ◽  
Current State ◽  
Challenges And Opportunities ◽  
Master's Thesis

This article is an expansion of Anastasov, T.’s master’s thesis (2019) and attempts to give a clear definition and taxonomy of the Data-Driven Business Models (DDBMs) as well as illustrate data challenges and opportunities that come along with this. These definitions were cross-analyzed with 3 cases from the Asia-Pacific region to deliver concrete insights and inspiration for Western companies to reinvent their businesses in the next 5 years. A comparison between Data-Driven and Data-Centric models was given as well, not previously analyzed in the thesis, as a view on the current state-of-the-art data business models.

scholarly journals AI-based Legal Technology: A Critical Assessment of the Current Use of Artificial Intelligence in Legal Practice

2021 ◽  
Vol 15 (2) ◽  
pp. 279-300
Author(s):  
Jana Soukupová
Keyword(s):  
Artificial Intelligence ◽  
Critical Reflection ◽  
State Of The Art ◽  
Legal Profession ◽  
Critical Assessment ◽  
Legal Practice ◽  
Current State ◽  
Legal Technology

In recent years, disruptive legal technology has been on the rise. Currently, several AI-based tools are being deployed across the legal field, including the judiciary. Although many of these innovative tools claim to make the legal profession more efficient and justice more accessible, we could have seen several critical voices against their use and even attempts to ban these services.  This article deals with the use of artificial intelligence in legal technology and offers a critical reflection on the current state of the art. As much as artificial intelligence proved that it could improve the legal profession, there are still some underlying risks connected to the technology itself, which may deem its use disturbing.

scholarly journals A Graphical Criterion for Effect Identification in Equivalence Classes of Causal Diagrams

2018 ◽  
Author(s):  
Amin Jaber ◽  
Jiji Zhang ◽  
Elias Bareinboim
Keyword(s):  
Observational Data ◽  
Equivalence Class ◽  
State Of The Art ◽  
Causal Structure ◽  
Learning Algorithms ◽  
Equivalence Classes ◽  
Data Driven ◽  
Current State ◽  
Causal Diagrams ◽  
Necessary And Sufficient

Computing the effects of interventions from observational data is an important task encountered in many data-driven sciences. The problem is addressed by identifying the post-interventional distribution with an expression that involves only quantities estimable from the pre-interventional distribution over observed variables, given some knowledge about the causal structure. In this work, we relax the requirement of having a fully specified causal structure and study the identifiability of effects with a singleton intervention (X), supposing that the structure is known only up to an equivalence class of causal diagrams, which is the output of standard structural learning algorithms (e.g., FCI). We derive a necessary and sufficient graphical criterion for the identifiability of the effect of X on all observed variables. We further establish a sufficient graphical criterion to identify the effect of X on a subset of the observed variables, and prove that it is strictly more powerful than the current state-of-the-art result on this problem.

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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