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.

Machine Learning

2012 ◽  
pp. 13-22 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Machine Learning

2011 ◽  
pp. 2462-2468 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Reorganizing Organizational Politics Research: A Review of the Literature and Identification of Future Research Directions

2019 ◽  
Vol 6 (1) ◽  
pp. 299-323 ◽  
Author(s):  
Gerald R. Ferris ◽  
B. Parker Ellen ◽  
Charn P. McAllister ◽  
Liam P. Maher
Keyword(s):  
Organizational Politics ◽  
Future Research ◽  
Review Of The Literature ◽  
Future Directions ◽  
Research Directions ◽  
Perceptions Of Organizational Politics ◽  
Political Outcomes ◽  
Future Research Directions ◽  
Political Actions ◽  
New Framework

Organizational politics has been an oft-studied phenomenon for nearly four decades. Prior reviews have described research in this stream as aligning with one of three categories: perceptions of organizational politics (POPs), political behavior, or political skill. We suggest that because these categories are at the construct level research on organizational politics has been artificially constrained. Thus, we suggest a new framework with higher-level categories within which to classify organizational politics research: political characteristics, political actions, and political outcomes. We then provide a broad review of the literature applicable to these new categories and discuss the possibilities for future research within each expanded category. Finally, we close with a discussion of future directions for organizational politics research across the categories.

Pedestrian falls: A review of the literature and future research directions

2017 ◽  
Vol 62 ◽  
pp. 227-234 ◽  
Author(s):  
Paul Schepers ◽  
Berry den Brinker ◽  
Rob Methorst ◽  
Marco Helbich
Keyword(s):  
Future Research ◽  
Review Of The Literature ◽  
Research Directions ◽  
Future Research Directions

Benzo[1,2-c;4,5-c′]Bis[1,2,5]Thiadiazole in Organic Optoelectronics: A Mini-Review

2015 ◽  
Vol 03 (01n02) ◽  
pp. 1540003 ◽  
Author(s):  
Teck Lip Dexter Tam ◽  
Jishan Wu
Keyword(s):  
Open Shell ◽  
Future Research ◽  
Computational Studies ◽  
Research Directions ◽  
Organic Optoelectronics ◽  
Research Activities ◽  
Future Research Directions ◽  
Organic Optoelectronic

In this paper, organic optoelectronic research activities in the field of benzo[1,2-c;4,5-c′]bis[1,2,5]thiadiazole (BBT)-based materials are reviewed. Synthetic pathways to the BBT core and its computational studies are described. Collective observations from separate reports suggest open-shell biradical nature of BBT-based materials. Future research directions for these materials are also described.

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.

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.

Stereotypes about Chicanas and Chicanos

2001 ◽  
Vol 29 (1) ◽  
pp. 55-90 ◽  
Author(s):  
Yolanda Flores Niemann
Keyword(s):  
Stereotype Threat ◽  
Mexican Descent ◽  
Future Research ◽  
Racial Awareness ◽  
Review Of The Literature ◽  
Research Directions ◽  
Social Stereotypes ◽  
The Family ◽  
Client Relationship ◽  
Future Research Directions

A review of the literature on stereotypes about Chicanas/os reveals that people of Mexican descent are perceived predominantly in derogatory terms, with the few positive terms primarily related to the centrality of the family for this ethnic community. This review also indicates that Chicanas/os themselves often endorse these stereotypes. However, the extant literature has not examined the counseling process in relation to consensual, social stereotypes of this ethnic group. This article serves to bridge that gap in the literature. Counselors are strongly encouraged to be cognizant of how stereotypes may affect Chicanas/os, especially in areas related to identity, risky behavior, stereotype threat, education, gender roles, and stigmatization. Counselors are encouraged to increase racial awareness as part of the mental health development of their Chicana/o clients. Counselors are particularly challenged to examine how their own conscious and unconscious stereotypes may affect the counselor-client relationship. Future research directions are also discussed.

scholarly journals The Shift to Gamification in Education: A Review on Dominant Issues

2020 ◽  
Vol 49 (1) ◽  
pp. 113-137
Author(s):  
Kingsley Ofosu-Ampong
Keyword(s):  
Education Research ◽  
Information Systems ◽  
Future Research ◽  
Research Directions ◽  
Research Activities ◽  
Starting Point ◽  
Future Research Directions ◽  
Methodological Approaches ◽  
Game Elements ◽  
The Impact

This article examines gamification literature on education since 2011. Using highlighted themes from Kirriemuir and McFarlane’s review on games and education as a starting point, the study identified 32 published papers. Furthermore, the study evaluated and identified previous conceptual and methodological approaches for evaluating gamification in education research. Using the identifying themes, the study discusses the development and use of gamification in education (Theme I), the application of gamification in education (Theme II), and the impact of gamification in education (Theme III) and propose that there is increased gamification and game elements research activities bridging the idea of gamified information systems in education and offering interesting opportunities for future research. The study concludes with future research directions for gamification in education.

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