scholarly journals Building and Evaluation of a Structured Representation of Pharmacokinetics Information Presented in SPCs: From Existing Conceptual Views of Pharmacokinetics Associated with Natural Language Processing to Object-oriented Design

2003 ◽  
Vol 10 (3) ◽  
pp. 271-280 ◽  
Author(s):  
C. Duclos-Cartolano
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Object Oriented ◽  
Object Oriented Design ◽  
Structured Representation

Structured Representation of Drug Indications: Lexical and Semantic Analysis and Object-Oriented Modeling

2000 ◽  
Vol 39 (01) ◽  
pp. 83-87 ◽  
Author(s):  
C. Duclos ◽  
A. Venot
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Semantic Analysis ◽  
Drug Prescription ◽  
Object Oriented ◽  
Knowledge Bases ◽  
Single File ◽  
Drug Knowledge ◽  
Processing Software ◽  
Structured Representation

Abstract:No standardized representation of drug indications is currently available that could be used in drug knowledge bases. We describe an object-oriented representation of indications that should make it possible to develop new tools for selecting drugs and checking prescriptions in computerized drug prescription systems. The model was developed using the results of a lexical and semantic analysis of drug indications, collected into a single file and processed using natural language processing software. It distinguishes both the diseases for which the drug may be given and the efficiency of the drug for a given indication. Two aspects of the model were evaluated: the differences if two independent evaluators filled the attributes independently and the loss of information induced by the use of the model. A system based on this model, making it possible for the physician to select all the drugs satisfying various criteria, is also presented.

NL-OOPS: from natural language to object oriented requirements using the natural language processing system LOLITA

1996 ◽  
Vol 2 (2) ◽  
pp. 161-187 ◽  
Author(s):  
L. MICH
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Processing System ◽  
Object Oriented ◽  
Core System ◽  
Natural Language Processing System ◽  
Language Analysis ◽  
Natural Language Requirements ◽  
Object Models

This paper describes NL-OOPS, a CASE tool that supports requirements analysis by generating object oriented models from natural language requirements documents. The full natural language analysis is obtained using as a core system the Natural Language Processing System LOLITA. The object oriented analysis module implements an algorithm for the extraction of the objects and their associations for use in creating object models.

Generating Use Case Models from Arabic User Requirements in a Semiautomated Approach Using a Natural Language Processing Tool

2015 ◽  
Vol 24 (2) ◽  
pp. 277-286 ◽  
Author(s):  
Nabil Arman ◽  
Sari Jabbarin
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Unified Modeling Language ◽  
Object Oriented ◽  
Software Systems ◽  
User Requirements ◽  
Use Case ◽  
Use Case Models ◽  
Natural Language Processing Tool

AbstractAutomated software engineering has attracted a large amount of research efforts. The use of object-oriented methods for software systems development has made it necessary to develop approaches that automate the construction of different Unified Modeling Language (UML) models in a semiautomated approach from textual user requirements. UML use case models represent an essential artifact that provides a perspective of the system under analysis or development. The development of such use case models is very crucial in an object-oriented development method. The main principles used in obtaining these models are described. A natural language processing tool is used to parse different statements of the user requirements written in Arabic to obtain lists of nouns, noun phrases, verbs, verb phrases, etc., that aid in finding potential actors and use cases. A set of steps that represent our approach for constructing a use case model are presented. Finally, the proposed approach is validated using an experiment involving a group of graduate students who are familiar with use case modeling.

scholarly journals Mining Cancer-related Information in Electronic Healthcare Records with Natural Language Processing

2018 ◽  
Vol 1 (1) ◽  
pp. 003-004
Author(s):  
Man Liu
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Relevant Information ◽  
The United States ◽  
Cancer Information ◽  
Free Text ◽  
Health Records ◽  
Related Information ◽  
Structured Representation

Cancer is in the midst of leading causes of death. In 2018, around 1,735,350 new cases of cancer were estimated and 609,640 people will die from cancer in the United States. A wealth of cancer-relevant information is conserved in a variety of types of healthcare records, for example, the electronic health records (EHRs). However, part of the critical information is organized in the free narrative text which hampers machine to interpret the information underlying the text. The development of artificial intelligence provides a variety of solutions to this plight. For example, the technology of natural language processing (NLP) has emerged bridging the gap between free text and structured representation of cancer information. Recently, several researchers have published their work on unearthing cancer-related information in EHRs based on the NLP technology. Apart from the traditional NLP methods, the development of deep learning helps EHRs mining go further.

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.

Sign in / Sign up

Export Citation Format

Share Document