scholarly journals SEMANTIC NETWORKS FOR ENGINEERING DESIGN: A SURVEY

2021 ◽  
Vol 1 ◽  
pp. 2621-2630
Author(s):  
Ji Han ◽  
Serhad Sarica ◽  
Feng Shi ◽  
Jianxi Luo
Keyword(s):  
Engineering Design ◽  
Language Processing ◽  
Large Scale ◽  
Design Research ◽  
Semantic Network ◽  
Semantic Networks ◽  
Future Research ◽  
Research Directions ◽  
Engineering Data ◽  
Future Research Directions

AbstractThere have been growing uses of semantic networks in the past decade, such as leveraging large-scale pre-trained graph knowledge databases for various natural language processing (NLP) tasks in engineering design research. Therefore, the paper provides a survey of the research that has employed semantic networks in the engineering design research community. The survey reveals that engineering design researchers have primarily relied on WordNet, ConceptNet, and other common-sense semantic network databases trained on non-engineering data sources to develop methods or tools for engineering design. Meanwhile, there are emerging efforts to mine large scale technical publication and patent databases to construct engineering-contextualized semantic network databases, e.g., B-Link and TechNet, to support NLP in engineering design. On this basis, we recommend future research directions for the construction and applications of engineering-related semantic networks in engineering design research and practice.

scholarly journals Semantic Networks for Engineering Design: State of the Art and Future Directions

2021 ◽  
pp. 1-45
Author(s):  
Ji Han ◽  
Serhad Sarica ◽  
Feng Shi ◽  
Jianxi Luo
Keyword(s):  
Engineering Design ◽  
Large Scale ◽  
Design Research ◽  
State Of The Art ◽  
Semantic Network ◽  
Semantic Networks ◽  
Knowledge Bases ◽  
Data Sources ◽  
Future Research ◽  
Support Engineering

Abstract In the past two decades, there has been increasing use of semantic networks in engineering design for supporting various activities, such as knowledge extraction, prior art search, idea generation and evaluation. Leveraging large-scale pre-trained graph knowledge databases to support engineering design-related natural language processing (NLP) tasks has attracted a growing interest in the engineering design research community. Therefore, this paper aims to provide a survey of the state-of-the-art semantic networks for engineering design and propositions of future research to build and utilize large-scale semantic networks as knowledge bases to support engineering design research and practice. The survey shows that WordNet, ConceptNet and other semantic networks, which contain common-sense knowledge or are trained on non-engineering data sources, are primarily used by engineering design researchers to develop methods and tools. Meanwhile, there are emerging efforts in constructing engineering and technical-contextualized semantic network databases, such as B-Link and TechNet, through retrieving data from technical data sources and employing unsupervised machine learning approaches. On this basis, we recommend six strategic future research directions to advance the development and uses of large-scale semantic networks for artificial intelligence applications in engineering design.

scholarly journals Compressing Large-Scale Transformer-Based Models: A Case Study on BERT

2021 ◽  
Vol 9 ◽  
pp. 1061-1080
Author(s):  
Prakhar Ganesh ◽  
Yao Chen ◽  
Xin Lou ◽  
Mohammad Ali Khan ◽  
Yin Yang ◽  
...  
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Research Attention ◽  
Model Compression ◽  
Future Research Directions ◽  
Or Applications

Abstract Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks. However, these models often have billions of parameters, and thus are too resource- hungry and computation-intensive to suit low- capability devices or applications with strict latency requirements. One potential remedy for this is model compression, which has attracted considerable research attention. Here, we summarize the research in compressing Transformers, focusing on the especially popular BERT model. In particular, we survey the state of the art in compression for BERT, we clarify the current best practices for compressing large-scale Transformer models, and we provide insights into the workings of various methods. Our categorization and analysis also shed light on promising future research directions for achieving lightweight, accurate, and generic NLP models.

Introduction to L2 attrition

2019 ◽  
pp. 329-348 ◽  
Author(s):  
Teodora H. Mehotcheva ◽  
Barbara Köpke
Keyword(s):  
Second Language ◽  
Large Scale ◽  
First Language ◽  
Foreign Languages ◽  
Future Research ◽  
Theoretical Frameworks ◽  
Research Directions ◽  
Short Overview ◽  
Future Research Directions ◽  
The Subject

As the introduction to the section on second language (L2) attrition, this chapter provides a broad presentation to research on attrition of L2 and foreign languages (FL). We will first discuss the terminology used in the field, focusing on some important differences in the terminology used in first language (L1) attrition studies. It provides a short overview of the development of the field, outlining major challenges and obstacles that research on the topic has to deal with. Next, it briefly describes the major findings and knowledge amassed on the subject before reviewing in more detail the findings of some of the most significant and large-scale projects carried out on L2/FL attrition. A final presentation of several theoretical frameworks of interest for L2/FL attrition research will allow us to show how L2/FL attrition is commonly explained but also to provide some ideas for future research directions.

Recent advances in methods of lexical semantic relatedness – a survey

2012 ◽  
Vol 19 (4) ◽  
pp. 411-479 ◽  
Author(s):  
ZIQI ZHANG ◽  
ANNA LISA GENTILE ◽  
FABIO CIRAVEGNA
Keyword(s):  
Language Processing ◽  
Semantic Relatedness ◽  
Background Information ◽  
Future Research ◽  
Review Of The Literature ◽  
Research Directions ◽  
Lexical Semantic ◽  
Research Activities ◽  
Domain Boundaries ◽  
Future Research Directions

AbstractMeasuring lexical semantic relatedness is an important task in Natural Language Processing (NLP). It is often a prerequisite to many complex NLP tasks. Despite an extensive amount of work dedicated to this area of research, there is a lack of an up-to-date survey in the field. This paper aims to address this issue with a study that is focused on four perspectives: (i) a comparative analysis of background information resources that are essential for measuring lexical semantic relatedness; (ii) a review of the literature with a focus on recent methods that are not covered in previous surveys; (iii) discussion of the studies in the biomedical domain where novel methods have been introduced but inadequately communicated across the domain boundaries; and (iv) an evaluation of lexical semantic relatedness methods and a discussion of useful lessons for the development and application of such methods. In addition, we discuss a number of issues in this field and suggest future research directions. It is believed that this work will be a valuable reference to researchers of lexical semantic relatedness and substantially support the research activities in this field.

scholarly journals Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

2020 ◽  
Author(s):  
Thanh Thi Nguyen
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Medical Image ◽  
Current Status ◽  
Future Research ◽  
Research Directions ◽  
Daily Lives ◽  
Related Data ◽  
Future Research Directions ◽  
The Internet Of Things

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

WeDesign: A Forum-Based Tool for Managing User-Generated Content in Engineering Design and Product Development

2008 ◽  
Author(s):  
Mark J. Jakiela ◽  
Jing Zheng
Keyword(s):  
Product Development ◽  
Engineering Design ◽  
User Generated Content ◽  
Future Research ◽  
Work Processes ◽  
Research Directions ◽  
Web Forum ◽  
Future Research Directions ◽  
Large Groups ◽  
Design And Product Development

A web forum-based tool for managing user-generated content in engineering design and product development is described. The system is intended to allow a “crowdsourcing” approach, in which large groups perform the work more commonly by individuals. User tests are conducted with an initial implementation, with the system configured in control and “parliamentary” modes. This experiment is done in the setting of a mechanical engineering senior capstone design course. The parliamentary mode is intended to encourage discussion and negotiation among participants, and allows them to design their own work processes. Review of the designs produced together with responses to a survey indicate the system was favorably received, and allowed a group to generate and select concept designs. Future research directions are suggested.

Implicit Knowledge Discovery in Design Semantic Network by Applying Pythagorean Means on Shortest Path Searching

2017 ◽  
Author(s):  
Feng Shi ◽  
Liuqing Chen ◽  
Ji Han ◽  
Peter Childs
Keyword(s):  
Text Mining ◽  
Knowledge Discovery ◽  
Language Processing ◽  
Shortest Path ◽  
Large Scale ◽  
Semantic Network ◽  
Semantic Networks ◽  
Implicit Knowledge ◽  
Implicit Associations ◽  
Correlation Degree

With the advent of the big-data era, massive textual information stored in electronic and digital documents have become valuable resources for knowledge discovery in the fields of design and engineering. Ontology technologies and semantic networks have been widely applied with text mining techniques including Natural Language Processing (NLP) to extract structured knowledge associations from the large-scale unstructured textual data. However, most existing works mainly focus on how to construct the semantic networks by developing various text mining methods such as statistical approaches and semantic approaches, while few studies are found to focus on how to subsequently analyze and fully utilize the already well-established semantic networks. In this paper, a specific network analysis method is proposed to discover the implicit knowledge associations from the existing semantic network for improving knowledge discovery and design innovation. Pythagorean means are applied with Dijkstra’s shortest path algorithm to discover the implicit knowledge associations either around a single knowledge concept or between two concepts. Six criteria are established to evaluate and rank the correlation degree of the implicit associations. Two engineering case studies were conducted to illustrate the proposed knowledge discovery process, and the results showed the effectiveness of the retrieved implicit knowledge associations on helping providing relevant knowledge from various aspects, and provoking creative ideas for engineering innovation.

scholarly journals Microdosing as a response to the meaning crisis

2020 ◽  
Author(s):  
Rotem Petranker ◽  
Juensung Kim ◽  
Thomas Anderson
Keyword(s):  
Large Scale ◽  
Online Survey ◽  
Meaning Making ◽  
Future Research ◽  
Free Text ◽  
Double Blind ◽  
Research Directions ◽  
Optimal Dosing ◽  
Social Media Platforms ◽  
Future Research Directions

Background: The use of psychedelic substances like LSD and magic mushrooms in research and to treat mental health conditions has been increasing in the last decade. In particular, the practice of microdosing – using sub-hallucinogenic amounts of psychedelics – has been increasing (Anderson et al., 2019), but large-scale systemic qualitative analyses are still scant.Aims: This study attempted to recognize emergent themes in qualitative reports regarding the experience of microdosing so as to enrich the theoretical landscape in psychedelics research and propose future research directions for both basic and clinical research.Methods: This study used qualitative analysis to analyze free-text responses from individuals who participated in an online survey disseminated on various social media platforms. Participants had reported microdosing at least once in the past year.Results: Data from 118 informative responses suggested four main emergent themes: reasons for microdosing, the practice of microdosing, outcomes linked to microdosing, and meta-commentary about microdosing. Participants mostly reported microdosing for clinical reasons and to improve productivity, and mentioned that the practice is often challenging due to unknown optimal dosing regimen. The outcomes of microdosing varied widely between strong endorsement of the practice and disappointment at the lack of effect. Meta-commentary included warning against overexcitement with the practice. We couch our findings in meaning-making theory and propose that, even at low doses, psychedelic substances can provide a sense of meaning currently lacking in Western culture.Conclusion: Our results suggest that there many of the reported benefits occur regardless of motivation to microdose and are likely due to the enhanced psychological flexibility and sense of connectedness made possible due to the use of psychedelics. Double-blind, placebo controlled experiments are required in order to substantiate these reports.

scholarly journals On Language Processing Shaping Decision Making

2017 ◽  
Vol 26 (2) ◽  
pp. 146-151 ◽  
Author(s):  
Albert Costa ◽  
Marc–Lluís Vives ◽  
Joanna D. Corey
Keyword(s):  
Decision Making ◽  
Foreign Language ◽  
Language Processing ◽  
Native Language ◽  
Dual System ◽  
Future Research ◽  
Research Directions ◽  
Decision Making Processes ◽  
Future Research Directions ◽  
The Impact

Recent research has revealed that people’s preferences, choices, and judgments are affected by whether information is presented in a foreign or a native language. Here, we review this evidence, focusing on various decision-making domains and advancing a variety of potential explanations for this foreign-language effect on decision making. We interpret the findings in the context of dual-system theories of decision making, entertaining the possibility that foreign-language processing reduces the impact of intuition and/or increases the impact of deliberation on people’s choices. In closing, we suggest future research directions for progressing our understanding of how language and decision-making processes interact when guiding people’s decisions.

Data Mining

2012 ◽  
pp. 75-94 ◽  
Author(s):  
Martin Atzmueller
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Classification ◽  
Pattern Mining ◽  
Data Sources ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions

Data Mining provides approaches for the identification and discovery of non-trivial patterns and models hidden in large collections of data. In the applied natural language processing domain, data mining usually requires preprocessed data that has been extracted from textual documents. Additionally, this data is often integrated with other data sources. This chapter provides an overview on data mining focusing on approaches for pattern mining, cluster analysis, and predictive model construction. For those, we discuss exemplary techniques that are especially useful in the applied natural language processing context. Additionally, we describe how the presented data mining approaches are connected to text mining, text classification, and clustering, and discuss interesting problems and future research directions.

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