scholarly journals How relevant is linguistics to computational linguistics?

2011 ◽  
Vol 6 ◽  
Author(s):  
Mark Johnson
Keyword(s):  
Machine Learning ◽  
Computational Linguistics ◽  
Linguistic Theory ◽  
Statistical Techniques ◽  
Engineering Applications ◽  
The Past ◽  
Statistical Parsing ◽  
Types Of Information ◽  
The Relationship ◽  
Scientific Fields

I start by explaining what I take computational linguistics to be, and discuss the relationship between its scientific side and its engineering applications. Statistical techniques have revolutionised many scientific fields in the past two decades, including computational linguistics. I describe the evolution of my own research in statistical parsing and how that lead me away from focusing on the details of any specific linguistic theory, and to concentrate instead on discovering which types of information (i.e., features) are important for specific linguistic processes, rather than on the details of exactly how this information should be formalised. I end by describing some of the ways that ideas from computational linguistics, statistics and machine learning may have an impact on linguistics in the future.

Review and Applications of Machine Learning and Artificial Intelligence in Engineering

2020 ◽  
pp. 1-12
Author(s):  
Melda Yucel ◽  
Gebrail Bekdaş ◽  
Sinan Melih Nigdeli
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Development Process ◽  
Current Trend ◽  
Engineering Applications ◽  
The Past ◽  
Applications Of Machine Learning ◽  
Basic Features

This chapter presents a summary review of development of Artificial Intelligence (AI). Definitions of AI are given with basic features. The development process of AI and machine learning is presented. The developments of applications from the past to today are mentioned and use of AI in different categories is given. Prediction applications using artificial neural network are given for engineering applications. Usage of AI methods to predict optimum results is the current trend and it will be more important in the future.

Clinical Linguistics and Phonetics in Bulgaria – from Isolation to Interdisciplinary Approach

2020 ◽  
Vol 29 (2) ◽  
pp. 295-301
Author(s):  
Gergana Padareva-Ilieva
Keyword(s):  
Computational Linguistics ◽  
Multidisciplinary Approach ◽  
Collaborative Work ◽  
Interdisciplinary Approach ◽  
Broad Context ◽  
Fast Growing ◽  
The Past ◽  
Communicative Disorders ◽  
Complicated Process ◽  
Scientific Fields

Clinical linguistics and phonetics is one of the fast growing scientific fields in the past decades. Its role is important either for developing methods and interdisciplinary ap-proach in linguistics and phonetics or studying the nature of communicative disorders. It could also include collaborative work with specialists from other fields as computational linguistics, neuroscience, etc. In this broad context clinical linguistics and phonetics is a challenge for the Humanities in Bulgaria. The reason is that with regard to interdiscipli-nary research in this area there is still much to be done. A few are the studies in Bulgaria which could be related to Clinical Linguistics and Phonetics but many of them have the disadvantage of a research in isolation, i.e. with no collaboration with the appropriate specialists. When it is up to communication an interdisciplinary and even multidisciplinary approach is needed having in mind that communication itself is a complicated process and its disorders are a challenge for all scientists who work in the field of communication.

scholarly journals On the relationship between research parasites and fairness in machine learning: challenges and opportunities

GigaScience ◽  
2021 ◽  
Vol 10 (12) ◽  
Author(s):  
Nicolás Nieto ◽  
Agostina Larrazabal ◽  
Victoria Peterson ◽  
Diego H Milone ◽  
Enzo Ferrante
Keyword(s):  
Machine Learning ◽  
Data Model ◽  
Learning Systems ◽  
Training Data ◽  
Model Construction ◽  
Daily Lives ◽  
The Past ◽  
Learning Challenges ◽  
Challenges And Opportunities ◽  
The Relationship

Abstract Machine learning systems influence our daily lives in many different ways. Hence, it is crucial to ensure that the decisions and recommendations made by these systems are fair, equitable, and free of unintended biases. Over the past few years, the field of fairness in machine learning has grown rapidly, investigating how, when, and why these models capture, and even potentiate, biases that are deeply rooted not only in the training data but also in our society. In this Commentary, we discuss challenges and opportunities for rigorous posterior analyses of publicly available data to build fair and equitable machine learning systems, focusing on the importance of training data, model construction, and diversity in the team of developers. The thoughts presented here have grown out of the work we did, which resulted in our winning the annual Research Parasite Award that GigaSciencesponsors.

scholarly journals Word Play

2007 ◽  
Vol 33 (4) ◽  
pp. 443-467
Author(s):  
Lauri Karttunen
Keyword(s):  
Computational Linguistics ◽  
Final Section ◽  
Question Answering ◽  
Word Play ◽  
The Past ◽  
Finite State Transducers ◽  
Finite State ◽  
History Of ◽  
Commercial Applications ◽  
The Relationship

This article is a perspective on some important developments in semantics and in computational linguistics over the past forty years. It reviews two lines of research that lie at opposite ends of the field: semantics and morphology. The semantic part deals with issues from the 1970s such as discourse referents, implicative verbs, presuppositions, and questions. The second part presents a brief history of the application of finite-state transducers to linguistic analysis starting with the advent of two-level morphology in the early 1980s and culminating in successful commercial applications in the 1990s. It offers some commentary on the relationship, or the lack thereof, between computational and paper-and-pencil linguistics. The final section returns to the semantic issues and their application to currently popular tasks such as textual inference and question answering.

scholarly journals Pattern Discovery in White Etching Crack Experimental Data Using Machine Learning Techniques

2019 ◽  
Vol 9 (24) ◽  
pp. 5502 ◽  
Author(s):  
Baher Azzam ◽  
Freia Harzendorf ◽  
Ralf Schelenz ◽  
Walter Holweger ◽  
Georg Jacobs
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Feature Selection Method ◽  
Chemical Compounds ◽  
Machine Learning Techniques ◽  
Oil Composition ◽  
The Past ◽  
Learning Techniques ◽  
Physical Tests ◽  
The Relationship

White etching crack (WEC) failure is a failure mode that affects bearings in many applications, including wind turbine gearboxes, where it results in high, unplanned maintenance costs. WEC failure is unpredictable as of now, and its root causes are not yet fully understood. While WECs were produced under controlled conditions in several investigations in the past, converging the findings from the different combinations of factors that led to WECs in different experiments remains a challenge. This challenge is tackled in this paper using machine learning (ML) models that are capable of capturing patterns in high-dimensional data belonging to several experiments in order to identify influential variables to the risk of WECs. Three different ML models were designed and applied to a dataset containing roughly 700 high- and low-risk oil compositions to identify the constituting chemical compounds that make a given oil composition high-risk with respect to WECs. This includes the first application of a purpose-built neural network-based feature selection method. Out of 21 compounds, eight were identified as influential by models based on random forest and artificial neural networks. Association rules were also mined from the data to investigate the relationship between compound combinations and WEC risk, leading to results supporting those of previous analyses. In addition, the identified compound with the highest influence was proved in a separate investigation involving physical tests to be of high WEC risk. The presented methods can be applied to other experimental data where a high number of measured variables potentially influence a certain outcome and where there is a need to identify variables with the highest influence.

scholarly journals CONCEPT OF SENTIMENT ANALYSIS USING MACHINE LEARNING TECHNIQUES

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Shuchita Mudgil ◽  
Prof Ashok Verma
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Machine Learning Techniques ◽  
Emotional States ◽  
The Past ◽  
Learning Techniques ◽  
Past Data

Sentiment analysis is used to conclude the approach of a consumer with respect to some topic. Sentimental analysis, a sub discipline within data mining and computational linguistics, refers to the methodology for mining, understanding the opinions expressed by the consumer in various forms like forums, forms blogs etc. The goal of sentiment analysis is to identify emotional states in online text. We Know human’s learns from past knowledge and machines follows instructions given by humans. But what if humans can prepare the machines from the past data and to put output to work much faster well that what is machine learning is it’s not about learning it’s also about understanding. So we will learn about analysis of sentiments using machine learning techniques

scholarly journals Meaning and structural dynamics in poetry: a computational perspective

2022 ◽  
Author(s):  
Alex Gomez-Marin
Keyword(s):  
Computational Linguistics ◽  
Dimensional Space ◽  
Mathematical Structure ◽  
Connectivity Matrix ◽  
Future Studies ◽  
The Past ◽  
Large Correlation ◽  
Low Dimensional ◽  
Main Ideas ◽  
The Relationship

This work addresses Sri Aurobindo’s mantric poem, Savitri, with a computational linguistics approach. This is one of the longest poems ever written in English. We build the connectivity matrix between all main word pairs and analyse its structure. Concepts emerge as directions that better explain the variance of the data in the hyperspace of words. When projected to the low dimensional space of concepts, the vector of attention as the reader moves through the text shows a large correlation across sections of the poem, thus acting the future and the past over again. These findings suggest that the mathematical structure of Savitri is and reflects a substrate for the author’s main ideas, facilitating the reader’s understanding of the poem’s meaning via its long-range dynamical correlations. Acknowledging an irreducible essence to poetry, future studies on the relationship between words and sounds, and sounds and ideas may provide invaluable hints of the origin of language and its intimate relationship with the evolution of human consciousness.

Poor Acceptance of the Past is Related to Depressive Symptoms in Older Adults

GeroPsych ◽  
2020 ◽  
Vol 33 (4) ◽  
pp. 246-251
Author(s):  
Gozde Cetinkol ◽  
Gulbahar Bastug ◽  
E. Tugba Ozel Kizil
Keyword(s):  
Older Adults ◽  
Regression Analysis ◽  
Depressive Symptoms ◽  
Death Anxiety ◽  
Geriatric Depression ◽  
Ego Integrity ◽  
The Past ◽  
Depressed Group ◽  
Total Death ◽  
The Relationship

Abstract. Depression in older adults can be explained by Erikson’s theory on the conflict of ego integrity versus hopelessness. The study investigated the relationship between past acceptance, hopelessness, death anxiety, and depressive symptoms in 100 older (≥50 years) adults. The total Beck Hopelessness (BHS), Geriatric Depression (GDS), and Accepting the Past (ACPAST) subscale scores of the depressed group were higher, while the total Death Anxiety (DAS) and Reminiscing the Past (REM) subscale scores of both groups were similar. A regression analysis revealed that the BHS, DAS, and ACPAST predicted the GDS. Past acceptance seems to be important for ego integrity in older adults.

A literature review on firms' internationalisation through e-commerce

2019 ◽  
pp. 121-143
Author(s):  
Riccardo Resciniti ◽  
Federica De Vanna
Keyword(s):  
Systematic Review ◽  
Literature Review ◽  
International Business ◽  
Decision Process ◽  
Foreign Markets ◽  
New Markets ◽  
Integrative Framework ◽  
The Past ◽  
The Relationship

The rise of e-commerce has brought considerable changes to the relationship between firms and consumers, especially within international business. Hence, understanding the use of such means for entering foreign markets has become critical for companies. However, the research on this issue is new and so it is important to evaluate what has been studied in the past. In this study, we conduct a systematic review of e-commerce and internationalisation studies to explicate how firms use e-commerce to enter new markets and to export. The studies are classified by theories and methods used in the literature. Moreover, we draw upon the internationalisation decision process (antecedents-modalities-consequences) to propose an integrative framework for understanding the role of e-commerce in internationalisation

Policy Making Introduction

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Nina TERREY ◽  
Sabine JUNGINGER
Keyword(s):  
Policy Implementation ◽  
Policy Making ◽  
Creative Industries ◽  
Design Management ◽  
Support Design ◽  
The Past ◽  
Academic Researchers ◽  
Design Activities ◽  
The Relationship ◽  
Professional Impact

The relationship that exists between design, policies and governance is quite complex and presents academic researchers continuously with new opportunities to engage and explore aspects relevant to design management. Over the past years, we have witnessed how the earlier focus on developing policies for design has shifted to an interest in understanding the ways in which design contributes to policy-making and policy implementation. Research into policies for design has produced insights into how policy-making decisions can advance professional impact and opportunities for designers and the creative industries. This research looked into how design researchers and design practitioners themselves can benefit from specific policies that support design activities and create the space for emerging design processes.

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