scholarly journals NLP-based personal learning assistant for school education

2021 ◽  
Vol 11 (5) ◽  
pp. 4522
Author(s):  
Ann Neethu Mathew ◽  
Rohini V. ◽  
Joy Paulose
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Intelligent Tutoring System ◽  
Digital Transformation ◽  
Educational Systems ◽  
Retrieval Performance ◽  
Language Understanding ◽  
Personal Learning ◽  
Computer Based ◽  
The Subject

Computer-based knowledge and computation systems are becoming major sources of leverage for multiple industry segments. Hence, educational systems and learning processes across the world are on the cusp of a major digital transformation. This paper seeks to explore the concept of an artificial intelligence and natural language processing (NLP) based intelligent tutoring system (ITS) in the context of computer education in primary and secondary schools. One of the components of an ITS is a learning assistant, which can enable students to seek assistance as and when they need, wherever they are. As part of this research, a pilot prototype chatbot was developed, to serve as a learning assistant for the subject Scratch (Scratch is a graphical utility used to teach school children the concepts of programming). By the use of an open source natural language understanding (NLU) or NLP library, and a slackbased UI, student queries were input to the chatbot, to get the sought explanation as the answer. Through a two-stage testing process, the chatbot’s NLP extraction and information retrieval performance were evaluated. The testing results showed that the ontology modelling for such a learning assistant was done relatively accurately, and shows its potential to be pursued as a cloud-based solution in future.

scholarly journals A Sequential and Intensive Weighted Language Modeling Scheme for Multi-Task Learning-Based Natural Language Understanding

2021 ◽  
Vol 11 (7) ◽  
pp. 3095
Author(s):  
Suhyune Son ◽  
Seonjeong Hwang ◽  
Sohyeun Bae ◽  
Soo Jun Park ◽  
Jang-Hwan Choi
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Empirical Investigation ◽  
Natural Language Understanding ◽  
Language Modeling ◽  
Language Understanding ◽  
Task Learning ◽  
Language Representation ◽  
Internal Transfer ◽  
Two Stages

Multi-task learning (MTL) approaches are actively used for various natural language processing (NLP) tasks. The Multi-Task Deep Neural Network (MT-DNN) has contributed significantly to improving the performance of natural language understanding (NLU) tasks. However, one drawback is that confusion about the language representation of various tasks arises during the training of the MT-DNN model. Inspired by the internal-transfer weighting of MTL in medical imaging, we introduce a Sequential and Intensive Weighted Language Modeling (SIWLM) scheme. The SIWLM consists of two stages: (1) Sequential weighted learning (SWL), which trains a model to learn entire tasks sequentially and concentrically, and (2) Intensive weighted learning (IWL), which enables the model to focus on the central task. We apply this scheme to the MT-DNN model and call this model the MTDNN-SIWLM. Our model achieves higher performance than the existing reference algorithms on six out of the eight GLUE benchmark tasks. Moreover, our model outperforms MT-DNN by 0.77 on average on the overall task. Finally, we conducted a thorough empirical investigation to determine the optimal weight for each GLUE task.

A WORD-BASED CHINESE LANGUAGE UNDERSTANDING SYSTEM

1988 ◽  
Vol 02 (01) ◽  
pp. 25-35
Author(s):  
TIAN-SHUN YAO
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Chinese Language ◽  
Computer Programs ◽  
World Knowledge ◽  
Knowledge Source ◽  
Language Understanding ◽  
Language Analysis ◽  
The World

With the word-based theory of natural language processing, a word-based Chinese language understanding system has been developed. In the light of psychological language analysis and the features of the Chinese language, this theory of natural language processing is presented with the description of the computer programs based on it. The heart of the system is to define a Total Information Dictionary and the World Knowledge Source used in the system. The purpose of this research is to develop a system which can understand not only Chinese sentences but also the whole text.

Guru

2012 ◽  
pp. 156-171 ◽  
Author(s):  
Andrew M. Olney ◽  
Natalie K. Person ◽  
Arthur C. Graesser
Keyword(s):  
Natural Language Processing ◽  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Natural Language Understanding ◽  
Natural Language Generation ◽  
Language Understanding ◽  
Language Generation ◽  
Processing Techniques

The authors discuss Guru, a conversational expert ITS. Guru is designed to mimic expert human tutors using advanced applied natural language processing techniques including natural language understanding, knowledge representation, and natural language generation.

scholarly journals Reasoning about Quantities in Natural Language

2015 ◽  
Vol 3 ◽  
pp. 1-13 ◽  
Author(s):  
Subhro Roy ◽  
Tim Vieira ◽  
Dan Roth
Keyword(s):  
Elementary School ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computational Approach ◽  
Quantitative Reasoning ◽  
Language Understanding ◽  
Numerical Reasoning ◽  
Key Steps

Little work from the Natural Language Processing community has targeted the role of quantities in Natural Language Understanding. This paper takes some key steps towards facilitating reasoning about quantities expressed in natural language. We investigate two different tasks of numerical reasoning. First, we consider Quantity Entailment, a new task formulated to understand the role of quantities in general textual inference tasks. Second, we consider the problem of automatically understanding and solving elementary school math word problems. In order to address these quantitative reasoning problems we first develop a computational approach which we show to successfully recognize and normalize textual expressions of quantities. We then use these capabilities to further develop algorithms to assist reasoning in the context of the aforementioned tasks.

scholarly journals CRITICAL ANALYSES OF NATURAL LANGUAGE PROCESSING (NLP) IN UNDERSTANDING SEMANTICS

World Science ◽  
2019 ◽  
Vol 1 (9(49)) ◽  
pp. 12-15
Author(s):  
Farida Huseynova
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Semantic Theory ◽  
Language Understanding

Today, language understanding systems do quite many useful things with processing natural language, even they are able to process the data much faster than humans are. Nevertheless, they do not have the same logical understanding of natural language yet as humans have and the interpretation capabilities of a language understanding system depending on the semantic theory is not sufficient in all aspects. The research is centered on some of the important issues that arise using it in natural language processing.

scholarly journals Using Edge AI and Language Understanding for Predictive Modeling of Acute Medical Intoxications

2021 ◽  
Vol 1 (2) ◽  
pp. 18-22
Author(s):  
Strahil Sokolov ◽  
Stanislava Georgieva
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Predictive Modeling ◽  
Personal Data ◽  
Experimental Results ◽  
Language Understanding ◽  
New Approach ◽  
Data Anonymization ◽  
Model Training

This paper presents a new approach to processing and categorization of text from patient documents in Bulgarian language using Natural Language Processing and Edge AI. The proposed algorithm contains several phases - personal data anonymization, pre-processing and conversion of text to vectors, model training and recognition. The experimental results in terms of achieved accuracy are comparable with modern approaches.

scholarly journals Natural Language Processing methods for Document Matching

2020 ◽  
Vol 6 (12) ◽  
pp. 379-383
Author(s):  
Maitri Patel and Dr Hemant D Vasava
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Original Work ◽  
Educational Systems ◽  
Processing Methods ◽  
Similar Test ◽  
Subject Examination ◽  
Different Types ◽  
Higher Educational

Data,Information or knoweldge,in this rapidly moving and growing world.we can find any kind of information on Internet.And this can be too useful,however for acedemic world too it is useful but along with it plagarism is highly in practice.Which makes orginality of work degrade and fraudly using someones original work and later not acknowleging them is becoming common.And some times teachers or professors could not identify the plagarised information provided.So higher educational systems nowadays use different types of tools to compare.Here we have an idea to match no of different documents like assignments of students to compare with each other to find out, did they copied each other’s work?Also an idea to compare ideal answeer sheet of particular subject examination to similar test sheets of students.Idea is to compare and on similarity basis we can rank them.Both approach is one kind and that is to compare documents.To identify plagarism there are many methods used already.So we could compare and develop them if needed.

Sign in / Sign up

Export Citation Format

Share Document