scholarly journals Resume Parser with Natural Language Processing

2021 ◽  
Author(s):  
Pornphat Sroison ◽  
Jonathan H. Chan
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Human Resource ◽  
Language Processing ◽  
Intelligent System ◽  
Job Description ◽  
Text Format ◽  
New Employees ◽  
Human Resource Department ◽  
Online Recruiting

<div>Because the online recruiting system has progressed, a large number of resumes were submitted. As a consequence, hiring new employees and reviewing a large number of resumes is a challenge for the human resource department or employer. Therefore, this system has helped employers by using an automated intelligent system based on natural language processing. This system can convert various formats of resumes to text format and can extract some important information. It is also possible to compare the applicant's resume and the job description to see the percentage of similarity as well. This system can assist the human resource department or employer in screening resumes before conducting interviews and finding the best candidate for the job position.</div>

scholarly journals Resume Parser with Natural Language Processing

2021 ◽  
Author(s):  
Pornphat Sroison ◽  
Jonathan H. Chan
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Human Resource ◽  
Language Processing ◽  
Intelligent System ◽  
Job Description ◽  
Text Format ◽  
New Employees ◽  
Human Resource Department ◽  
Online Recruiting

<div>Because the online recruiting system has progressed, a large number of resumes were submitted. As a consequence, hiring new employees and reviewing a large number of resumes is a challenge for the human resource department or employer. Therefore, this system has helped employers by using an automated intelligent system based on natural language processing. This system can convert various formats of resumes to text format and can extract some important information. It is also possible to compare the applicant's resume and the job description to see the percentage of similarity as well. This system can assist the human resource department or employer in screening resumes before conducting interviews and finding the best candidate for the job position.</div>

scholarly journals Digiprescription: An Intelligent System to Enable Paperless Prescription using Mobile Computing and Natural-language Processing

2020 ◽  
Author(s):  
Richard Zhang ◽  
Mary Zhao ◽  
Yucheng Jiang ◽  
Sophadeth Rithya ◽  
Yu Sun
Keyword(s):  
Natural Language Processing ◽  
Side Effects ◽  
Natural Language ◽  
Mobile Computing ◽  
Language Processing ◽  
Intelligent System ◽  
Drug Information ◽  
The Elderly ◽  
Black Box ◽  
Patient’S Information

Through our app, it is aimed to teach and tell the patients how to use the drug properly taking off the chances of putting their lives in danger, especially the elderly. It is also efficient to give patients these instructions as well as saving lots of paper. Because of the law, every drug that is given from the pharmacy to the user includes a receipt that lists information of, patient’s information, drug information, insurance information, directions on taking the medicine (black box warning issued by FDA), medication details on how it works, side effects, storage rules, and etc. These pieces of information are crucial to patients, where it tells them how to use the drug properly, but most people would throw these receipts away, which is a risk as well as a waste. Through using this app, the patient can efficiently get information on how to properly use the drug. This application is also helpful, where the user can choose to set reminders on when to eat this drug each week or month.

scholarly journals Natural Language Processing for Forecasting Innovative Development of the Energy Infrastructure

2020 ◽  
Vol 209 ◽  
pp. 03015
Author(s):  
Alex Kopaygorodsky
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Knowledge Integration ◽  
Semantic Analysis ◽  
Intelligent System ◽  
Scientific Research ◽  
Innovative Development ◽  
Energy Infrastructure

The article deals with the application of natural language processing methods to support research and forecasting the innovative development of energy infrastructure. The main methods of NLP, which are used to build an intelligent system to support scientific research, are considered. Methods of building infrastructure for processing Open Linked Data and Big Data are described. Semantic analysis and knowledge integration are based on ontology system. Applying suggested methods allow increasing quality of scientific research in this area and make it more effectively

scholarly journals Smart Dissemination by Using Natural Language Processing Technology

2020 ◽  
Vol 6 (2) ◽  
pp. 133
Author(s):  
Tora Fahrudin ◽  
Kastaman Kastaman ◽  
Sherin Nadya Meideni ◽  
Padma Edhitya Chairunnisafa Priyono ◽  
Muhammad Galang Fathirkina ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Intelligent System ◽  
Application Programming Interface ◽  
Message Content ◽  
Dynamic Content ◽  
Before And After ◽  
Dynamic Filter ◽  
Pos Tagger

Background: Recently, WhatsApp has become the world's most popular text and voice messaging application with 1.5 billion users. A lot of WhatsApp Application Programming Interface (API) has also been established to be connected to other applications. On the other hand, the development of natural language processing (NLP) for WhatsApp messages has snowballed. There are extensive studies on the dissemination information using WhatsApp but the study on NLP involving data from WhatsApp is lacking.Objective: This study aims to implement NLP in smart dissemination applications by using WhatsApp API.Methods: We build a framework that embeds an intelligent system based on the NLP in WhatsApp API to disseminate a dynamic message. Some of the sentences are used to evaluate the accuracy of this application.Results: Smart dissemination consists of dynamic filter and dynamic content. Dynamic filter was conducted by using the POS tagger and clause statement. Meanwhile, dynamic content was built by using the replace MySQL function. There are twofold limitation: the application could not transform a message that matches rule <3> with conjunction “dan”; has the same attribute before and after <CC> tag; and the maximum of the logical operator is one type for coordinating conjunction (AND/OR) in one sentence.Conclusion: Our framework can be used for dynamic dissemination of messages using dynamic message content and dynamic message recipient with an accuracy of 95% from twenty sample messages.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

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