scholarly journals Automated Essay Scoring: A Survey of the State of the Art

2019 ◽  
Author(s):  
Zixuan Ke ◽  
Vincent Ng
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
The State ◽  
Educational Values ◽  
Automated Essay Scoring ◽  
Research Challenges ◽  
Essay Scoring ◽  
Made In

Despite being investigated for over 50 years, the task of automated essay scoring is far from being solved. Nevertheless, it continues to draw a lot of attention in the natural language processing community in part because of its commercial and educational values as well as the associated research challenges. This paper presents an overview of the major milestones made in automated essay scoring research since its inception.

scholarly journals Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

2021 ◽  
Vol 3 ◽  
Author(s):  
Marieke van Erp ◽  
Christian Reynolds ◽  
Diana Maynard ◽  
Alain Starke ◽  
Rebeca Ibáñez Martín ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Interdisciplinary Approach ◽  
Comprehensive Analysis ◽  
The State ◽  
Use Cases

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

scholarly journals Automated Essay Scoring using Ontology Generator and Natural Language Processing with Question Generator based on Blooms Taxonomy’s Cognitive Level

2019 ◽  
Vol 9 (1) ◽  
pp. 2448-2457
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Ridge Regression ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Lasso Regression ◽  
Automated Essay Scoring ◽  
Essay Scoring

Essay writing examination is commonly used learning activity in all levels of education and disciplines. It is advantageous in evaluating the student’s learning outcomes because it gives them the chance to exhibit their knowledge and skills freely. For these reasons, a lot of researchers turned their interest in Automated essay scoring (AES) is one of the most remarkable innovations in text mining using Natural Language Processing and Machine learning algorithms. The purpose of this study is to develop an automated essay scoring that uses ontology and Natural Language Processing. Different learning algorithms showed agreeing prediction outcomes but still regression algorithm with the proper features incorporated with it may produce more accurate essay score. This study aims to increase the accuracy, reliability and validity of the AES by implementing the Gradient ridge regression with the domain ontology and other features. Linear regression, linear lasso regression and ridge regression were also used in conjunction with the different features that was extracted. The different features extracted are the domain concepts, average word length, orthography (spelling mistakes), grammar and sentiment score. The first dataset used is the ASAP dataset from Kaggle website is used to train and test different machine learning algorithms that is consist of linear regression, linear lasso regression, ridge regression and gradient boosting regression together with the different features identified. The second dataset used is the one extracted from the student’s essay exam in Human Computer Interaction course. The results show that the Gradient Boosting Regression has the highest variance and kappa scores. However, we can tell that there are similarities when it comes to performances for Linear, Ridge and Lasso regressions due to the dataset used which is ASAP. Furthermore, the results were evaluated using Cohen Weighted Kappa (CWA) score and compared the agreement between the human raters. The CWA result is 0.659 that can be interpreted as Strong level of agreement between the Human Grader and the automated essay score. Therefore, the proposed AES has 64-81% reliability level.

A text-based multi-span network for reading comprehension

2021 ◽  
pp. 1-13
Author(s):  
Deguang Chen ◽  
Ziping Ma ◽  
Lin Wei ◽  
Yanbin Zhu ◽  
Jinlin Ma ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Market Value ◽  
The State ◽  
Experimental Results ◽  
Training Time ◽  
The Public

Text-based reading comprehension models have great research significance and market value and are one of the main directions of natural language processing. Reading comprehension models of single-span answers have recently attracted more attention and achieved significant results. In contrast, multi-span answer models for reading comprehension have been less investigated and their performances need improvement. To address this issue, in this paper, we propose a text-based multi-span network for reading comprehension, ALBERT_SBoundary, and build a multi-span answer corpus, MultiSpan_NMU. We also conduct extensive experiments on the public multi-span corpus, MultiSpan_DROP, and our multi-span answer corpus, MultiSpan_NMU, and compare the proposed method with the state-of-the-art. The experimental results show that our proposed method achieves F1 scores of 84.10 and 92.88 on MultiSpan_DROP and MultiSpan_NMU datasets, respectively, while it also has fewer parameters and a shorter training time.

An Overview of Shallow and Deep Natural Language Processing for Ontology Learning

2011 ◽  
pp. 16-37 ◽  
Author(s):  
Amal Zouaq
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Process ◽  
Semantic Analysis ◽  
State Of The Art ◽  
Knowledge Extraction ◽  
The State ◽  
Ontology Learning ◽  
Analysis Methods

This chapter gives an overview over the state-of-the-art in natural language processing for ontology learning. It presents two main NLP techniques for knowledge extraction from text, namely shallow techniques and deep techniques, and explains their usefulness for each step of the ontology learning process. The chapter also advocates the interest of deeper semantic analysis methods for ontology learning. In fact, there have been very few attempts to create ontologies using deep NLP. After a brief introduction to the main semantic analysis approaches, the chapter focuses on lexico-syntactic patterns based on dependency grammars and explains how these patterns can be considered as a step towards deeper semantic analysis. Finally, the chapter addresses the “ontologization” task that is the ability to filter important concepts and relationships among the mass of extracted knowledge.

Morphological disambiguation of Hebrew: a case study in classifier combination

2012 ◽  
Vol 20 (1) ◽  
pp. 69-97 ◽  
Author(s):  
GENNADI LEMBERSKY ◽  
DANNY SHACHAM ◽  
SHULY WINTNER
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Morphological Analysis ◽  
State Of The Art ◽  
The State ◽  
Classifier Combination ◽  
Complex Morphology ◽  
Percent Accuracy

AbstractMorphological analysis and disambiguation are crucial stages in a variety of natural language processing applications, especially when languages with complex morphology are concerned. We present a system which disambiguates the output of a morphological analyzer for Hebrew. It consists of several simple classifiers and a module that combines them under the constraints imposed by the analyzer. We explore several approaches to classifier combination, as well as a back-off mechanism that relies on a large unannotated corpus. Our best result, around 83 percent accuracy, compares favorably with the state of the art on this task.

Introduction

2006 ◽  
Vol 12 (2) ◽  
pp. 109-113
Author(s):  
JILL BURSTEIN ◽  
CLAUDIA LEACOCK
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Educational Software ◽  
Intelligent Tutoring ◽  
Classroom Teachers ◽  
Automated Essay Scoring ◽  
Essay Scoring

Researchers and developers of educational software have experimented with natural language processing (NLP) capabilities and related technologies since the 1960's. Automated essay scoring was perhaps the first application of this kind (Page 1966). Over a decade later, Writer's Workbench, a text-editing application, was developed as a tool for classroom teachers (MacDonald, Frase, Gingrich and Keenan 1982). Intelligent tutoring applications, though more in the spirit of artificial intelligence, were also being developed during this time (Carbonell 1970; Brown, Burton and Bell 1974; Stevens and Collins 1977; Burton and Brown 1982; Clancy 1987).

scholarly journals Prediction of suicidal ideation in young people from the analysis of texts in social networks written in Mexican spanish: a review of the state of the art

2020 ◽  
pp. 29-33
Author(s):  
Gabriel AGUILERA-GONZÁLEZ ◽  
Christian PADILLA-NAVARRO ◽  
Carlos ZARATE-TREJO ◽  
Georges KHALAF
Keyword(s):  
Social Networks ◽  
Natural Language Processing ◽  
Early Detection ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
The State ◽  
World Health ◽  
Mexican Spanish ◽  
The World

Suicide prevention is one of the great issues of the current era. Institutions such as the World Health Organization, have continued to search for all possible alternatives for early detection and timely prevention. Suicide rates have grown more and more in the world, and Mexico, although it is not the country with the most suicides, is one of the countries with the highest growth in recent years. At present, the use of social networks has generated great changes in the way we communicate. Expressing yourself through a social network begins to be more common than expressing ourselves to human beings. Several studies, which will be presented later, show that it is possible to determine from the content of social networks: cases of depression, risk of suicide, and other mental problems. The use of technological tools, such as Natural Language Processing, has served as an effective ally for the early detection of risks, such as abuse, bullying or even detecting emotional problems. The present research seeks to carry out an in-depth analysis in the state of the art of the application of Natural Language Processing as an ally for the detection of suicide risk from the analysis of texts for Mexican Spanish in Social Networks.

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