scholarly journals Improving Email Response in an Email Management System Using Natural Language Processing Based Probabilistic Methods

2015 ◽  
Vol 11 (1) ◽  
pp. 109-119 ◽  
Author(s):  
Abdulkareem Al-Alwani
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Management System ◽  
Probabilistic Methods ◽  
Email Management

scholarly journals AI NLP Chatbot for HR and Employee Support

2019 ◽  
Vol 8 (9S3) ◽  
pp. 580-582
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Management System ◽  
Work Process ◽  
Employee Support ◽  
Administrative Work ◽  
Management Application

This task is planned for building up an online chatbot for leave the executives framework that is of significance to either an association. The Leave Management System (LMS) is an Intranet based application that can be gotten to all through the association or a predefined gathering/Dept. In this,we utilized Natural Language Processing(NLP) which is a part of Artificial Intelligence(AI).With NLP[1] the cooperation among PC and people are possible.This framework can be utilized to mechanize the work process of leave applications and their endorsements. The occasional crediting of leave is additionally automated.There are highlights like email notices, programmed endorsement of leave, report generators and so on in this framework. Leave Management application will lessen administrative work and keeps up record in progressively proficient manner. This framework will redesign the procedure of leave the board inside organization by sparing time and assets. The Leave Management System serves to workers can view leave adjusts, demand leaves, see past leave history and chief can affirm leave applications.The talk bot will give every one of the reactions what the client inquired

scholarly journals A Hybrid Multi-Word Terms Extraction System Applied to Topic Detection

2014 ◽  
Vol 13 (10) ◽  
pp. 5105-5112 ◽  
Author(s):  
Rim Koulali ◽  
Abdelouafi Meziane
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Probabilistic Methods ◽  
Extraction System ◽  
Topic Detection ◽  
Part Of Speech ◽  
Log Likelihood ◽  
C Value ◽  
Morphological Patterns

Mutli-word Terms extraction plays an important role in many Natural Language Processing (NLP) tasks. Despite their major importance, few works were dedicated to Arabic multi-word terms extraction. This paper proposes an automatic Arabic multi-word terms (MWTs) extraction system based on two major filtering steps: linguistics filter using a part-of-speech tagger along with morphological patterns and statistical filter based on probabilistic methods, namely: Log-Likelihood Ratio (LLR) and C-value. We evaluate the performances of the realized systems on Wattan; an Arabic oriented topic newspaper corpus. Our system manages to achieve 90.23% in term of multi-word extraction precision. We also study the use of MWTs as features in Arabic Topic Detection. The conducted experiments show good results.

scholarly journals Artificially Intelligent Response based Utility Management System

2019 ◽  
Vol 8 (2) ◽  
pp. 5038-5040
Keyword(s):  
Natural Language Processing ◽  
Speech Recognition ◽  
Natural Language ◽  
Language Processing ◽  
Management System ◽  
Speech Synthesis ◽  
Data Sets ◽  
Private Data ◽  
Generation Mechanisms ◽  
Processing Techniques

Every organization should have its very own Personal AI Assistant at its disposal. The aim of the assistant is to find a knowledge gap to fill by its well-structured speech synthesis and generation mechanisms. The inquiry or interrogation can be predominantly generic to the requirements of the institution. The assistant will understand based on the clarity of purpose. Responses are original, innovative, clear and concise as its vital to provide correct up to date information. They can also be visual and graphical based on the question. Extracted data from private data sets of the organization are analyzed and organized using Pandas. Speech Recognition, Understanding, and Synthesis are done using Speech Recognition Packages and Natural Language Processing Techniques.

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.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

2020 ◽  
Author(s):  
Vadim V. Korolev ◽  
Artem Mitrofanov ◽  
Kirill Karpov ◽  
Valery Tkachenko
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Science ◽  
Therapeutic Agent ◽  
Semantic Relations ◽  
Chemical Data ◽  
Processing Methods ◽  
Modern Natural

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.

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