scholarly journals Practical Language Processing for Virtual Humans

2021 ◽  
Vol 24 (2) ◽  
pp. 1740-1747
Author(s):  
Anton Leuski ◽  
David Traum
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Virtual Human ◽  
Virtual Humans ◽  
Text Input ◽  
Processing Component ◽  
User Friendly ◽  
Language Classification ◽  
Human Systems

NPCEditor is a system for building a natural language processing component for virtual humans capable of engaging a user in spoken dialog on a limited domain. It uses a statistical language classification technology for mapping from user's text input to system responses. NPCEditor provides a user-friendly editor for creating effective virtual humans quickly. It has been deployed as a part of various virtual human systems in several applications.

scholarly journals NPCEditor: Creating Virtual Human Dialogue Using Information Retrieval Techniques

AI Magazine ◽  
2011 ◽  
Vol 32 (2) ◽  
pp. 42 ◽  
Author(s):  
Anton Leuski ◽  
David Traum
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Virtual Human ◽  
Virtual Humans ◽  
Text Input ◽  
Processing Component ◽  
User Friendly ◽  
Language Classification

NPCEditor is a system for building a natural language processing component for virtual humans capable of engaging a user in spoken dialog on a limited domain. It uses statistical language classification technology for mapping from a user’s text input to system responses. NPCEditor provides a user-friendly editor for creating effective virtual humans quickly. It has been deployed as a part of various virtual human systems in several applications.

FindThatQuote: A Question-Answering Web-based System to Locate Quotes using Deep Learning and Natural-Language Processing

2021 ◽  
Author(s):  
Nathan Ji ◽  
Yu Sun
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Success Rate ◽  
Language Processing ◽  
Question Answering ◽  
Specific Information ◽  
Web Based ◽  
User Friendly ◽  
Web Based System

The digital age gives us access to a multitude of both information and mediums in which we can interpret information. A majority of the time, many people find interpreting such information difficult as the medium may not be as user friendly as possible. This project has examined the inquiry of how one can identify specific information in a given text based on a question. This inquiry is intended to streamline one's ability to determine the relevance of a given text relative to his objective. The project has an overall 80% success rate given 10 articles with three questions asked per article. This success rate indicates that this project is likely applicable to those who are asking for content level questions within an article.

scholarly journals Natural language processing for cognitive therapy: Extracting schemas from thought records

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257832
Author(s):  
Franziska Burger ◽  
Mark A. Neerincx ◽  
Willem-Paul Brinkman
Keyword(s):  
Mental Health ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Support Vector ◽  
Free Text ◽  
Cognitive Approach ◽  
Text Input

The cognitive approach to psychotherapy aims to change patients’ maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. The schemas underlying such thought records have, thus far, been largely manually identified. Using recent advances in natural language processing, we take this one step further by automatically extracting schemas from thought records. To this end, we asked 320 healthy participants on Amazon Mechanical Turk to each complete five thought records consisting of several utterances reflecting cognitive processes. Agreement between two raters on manually scoring the utterances with respect to how much they reflect each schema was substantial (Cohen’s κ = 0.79). Natural language processing software pretrained on all English Wikipedia articles from 2014 (GLoVE embeddings) was used to represent words and utterances, which were then mapped to schemas using k-nearest neighbors algorithms, support vector machines, and recurrent neural networks. For the more frequently occurring schemas, all algorithms were able to leverage linguistic patterns. For example, the scores assigned to the Competence schema by the algorithms correlated with the manually assigned scores with Spearman correlations ranging between 0.64 and 0.76. For six of the nine schemas, a set of recurrent neural networks trained separately for each of the schemas outperformed the other algorithms. We present our results here as a benchmark solution, since we conducted this research to explore the possibility of automatically processing qualitative mental health data and did not aim to achieve optimal performance with any of the explored models. The dataset of 1600 thought records comprising 5747 utterances is published together with this article for researchers and machine learning enthusiasts to improve upon our outcomes. Based on our promising results, we see further opportunities for using free-text input and subsequent natural language processing in other common therapeutic tools, such as ecological momentary assessments, automated case conceptualizations, and, more generally, as an alternative to mental health scales.

scholarly journals Home Automation Using Chatbot and Voice Assistant

2020 ◽  
Vol 32 ◽  
pp. 01002
Author(s):  
Bhavyasri Kadali ◽  
Neha Prasad ◽  
Pranaya Kudav ◽  
Manoj Deshpande
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Raspberry Pi ◽  
Automation System ◽  
Home Automation ◽  
Voice Input ◽  
Text Input ◽  
Automation Systems ◽  
Home Automation System

In a world with ever increasing needs for comfort, human race is relying more and more on technological advancements to find solutions to their problems. Home Automation Systems have become a go-to arena in the recent years. In the following paper, we propose a Home Automation system that uses a wholesome blending of some technologies like Internet of Things, Natural Language Processing and Machine Learning. The prime feature of this system is that, it provides two modes of communication to the user : Text and Voice. The text input from the user will be given via a Chatbot Application and the voice input from the user will be given via a voice assistant. The input will undergo Natural Language Processing to find the action that the user wants the system to perform. The IoT component, Raspberry Pi would perform the actuations in the form of switching On or Off of Lights and Fans of a room in the house.

scholarly journals Predicting the Performance of Parsing with Referential Translation Machines

2016 ◽  
Vol 106 (1) ◽  
pp. 31-44
Author(s):  
Ergun Biçici
Keyword(s):  
Natural Language Processing ◽  
Lower Bound ◽  
Natural Language ◽  
Machine Translation ◽  
Semantic Similarity ◽  
Language Processing ◽  
Prediction Error ◽  
Prediction Performance ◽  
Text Input ◽  
Translation Machine

Abstract Referential translation machine (RTM) is a prediction engine used for predicting the performance of natural language processing tasks including parsing, machine translation, and semantic similarity pioneering language, task, and domain independence. RTM results for predicting the performance of parsing (PPP) in out-of-domain or in-domain settings with different training sets and types of features present results independent of language or parser. RTM PPP models can be used without parsing using only text input and without any parser or language dependent information. Our results detail prediction performance, top selected features, and lower bound on the prediction error of PPP.

scholarly journals An Automated Multiple-Choice Question Generation using Natural Language Processing Techniques

2021 ◽  
Vol 10 (02) ◽  
pp. 1-10
Author(s):  
Chidinma A. Nwafor ◽  
Ikechukwu E. Onyenwe
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Multiple Choice ◽  
Automatic Generation ◽  
Multiple Choice Question ◽  
Question Generation ◽  
Computer Based ◽  
Processing Techniques ◽  
User Friendly

Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data. Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are important words in a given lesson material. To validate that the system is not perverse, five lesson materials were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the teacher were compared to the auto-generated keywords and the result shows that the system was capable of extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a user-friendly interface for easy accessibility.

scholarly journals EXPERIMENTED KINETIC ENERGY AS FEATURES FOR NATURAL LANGUAGE CLASSIFICATION

2019 ◽  
Author(s):  
Daia Alexandru
Keyword(s):  
Natural Language Processing ◽  
Kinetic Energy ◽  
Natural Language ◽  
Language Processing ◽  
Feature Engineering ◽  
Feature Importance ◽  
Psychology And Medicine ◽  
Language Classification

This article describes various uses of kinetic Energy in Natural Language Processing (NLP) and whyNatural Language Processing could be used in trading, with the potential to be use also in otherapplications, including psychology and medicine. Kinetic energy discovered by great Romanianmathematician Octave Onicescu (1892-1983), allows to do feature engineering in various domainsincluding NLP which we did in this experiment. More than that we have run a machine learningmodel called xgboost to see feature importance and the features extracted by xgboost where capturedthe most important, in order to classify for simplicity of reader some authors by their content and typeof writing

scholarly journals CovidNLP: A Web Application for Distilling Systemic Implications of COVID-19 Pandemic with Natural Language Processing

2020 ◽  
Author(s):  
Raghav Awasthi ◽  
Ridam Pal ◽  
Pradeep Singh ◽  
Aditya Nagori ◽  
Suryatej Reddy ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Web Application ◽  
Data Availability ◽  
Research Articles ◽  
Global Literature ◽  
Link Type ◽  
Novel Coronavirus ◽  
User Friendly

AbstractThe flood of conflicting COVID-19 research has revealed that COVID-19 continues to be an enigma. Although more than 14,000 research articles on COVID-19 have been published with the disease taking a pandemic proportion, clinicians and researchers are struggling to distill knowledge for furthering clinical management and research. In this study, we address this gap for a targeted user group, i.e. clinicians, researchers, and policymakers by applying natural language processing to develop a CovidNLP dashboard in order to speed up knowledge discovery. The WHO has created a repository of about more than 5000 peer-reviewed and curated research articles on varied aspects including epidemiology, clinical features, diagnosis, treatment, social factors, and economics. We summarised all the articles in the WHO Database through an extractive summarizer followed by an exploration of the feature space using word embeddings which were then used to visualize the summarized associations of COVID-19 as found in the text. Clinicians, researchers, and policymakers will not only discover the direct effects of COVID-19 but also the systematic implications such as the anticipated rise in TB and cancer mortality due to the non-availability of drugs during the export lockdown as highlighted by our models. These demonstrate the utility of mining massive literature with natural language processing for rapid distillation and knowledge updates. This can help the users understand, synthesize, and take pre-emptive action with the available peer-reviewed evidence on COVID-19. Our models will be continuously updated with new literature and we have made our resource CovidNLP publicly available in a user-friendly fashion at http://covidnlp.tavlab.iiitd.edu.in/.Data Availability StatementAll the data used in this study are publicly available from the WHO Covid-19 Global Literature on coronavirus disease maintained at https://search.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/. Our analysis and the interactive resource CovidNLP is publicly available in a user friendly fashion at http://covidnlp.tavlab.iiitd.edu.in

Sign in / Sign up

Export Citation Format

Share Document