Variations in terminology

Terminology ◽  
2010 ◽  
Vol 16 (1) ◽  
pp. 30-50 ◽  
Author(s):  
Anne Condamines
Keyword(s):  
Risk Management ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Language Use ◽  
Knowledge Engineering ◽  
Field Of Study ◽  
New Approach ◽  
Individual Variations ◽  
Definition Of

The study of variation in terminology came to the fore over the last fifteen years in connection with advances in textual terminology. This new approach to terminology could be a way of improving the management of risk related to language use in the workplace and to contribute to the definition of a “linguistics of the workplace”. As a theoretical field of study, linguistics has hardly found any application in the workplace. Two of its applied branches, however, Sociolinguistics and Natural Language Processing (NLP) are relevant. Both deal with lexical phenomena, — i.e. terminology — sociolinguistics taking into account very subtle inter-individual variations and NLP being more interested in stability in the use. So, taking into account variations in building terminologies could be a means of considering both description and prescription, use and norm. This approach to terminology, which has been made possible thanks to NLP and Knowledge Engineering could be a way of meeting needs in the workplace concerning risk management related to language use.

Constructing Corpora for the Development and Evaluation of Paraphrase Systems

2008 ◽  
Vol 34 (4) ◽  
pp. 597-614 ◽  
Author(s):  
Trevor Cohn ◽  
Chris Callison-Burch ◽  
Mirella Lapata
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Syntactic Structure ◽  
Nominal Data ◽  
Parallel Corpus ◽  
Word Alignments ◽  
Definition Of

Automatic paraphrasing is an important component in many natural language processing tasks. In this article we present a new parallel corpus with paraphrase annotations. We adopt a definition of paraphrase based on word alignments and show that it yields high inter-annotator agreement. As Kappa is suited to nominal data, we employ an alternative agreement statistic which is appropriate for structured alignment tasks. We discuss how the corpus can be usefully employed in evaluating paraphrase systems automatically (e.g., by measuring precision, recall, and F1) and also in developing linguistically rich paraphrase models based on syntactic structure.

A KNOWLEDGE-BASED CHINESE LETTER-WRITER

1988 ◽  
Vol 02 (01) ◽  
pp. 37-48
Author(s):  
KOH TOH TZU
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Knowledge Engineering ◽  
Systems Science ◽  
Form Processing ◽  
Design And Implementation ◽  
Knowledge Based ◽  
Chinese Business ◽  
Letter Writer

Since the end of last year, the researchers at the Institute of Systems Science (ISS) started to consider a more ambitious project as part of its multilingual programming objective. This project examines the domain of Chinese Business Letter Writing. With the problem defined as generating Chinese letters to meet business needs, investigations suggest an intersection of 3 possible approaches: knowledge engineering, form processing and natural language processing. This paper attempts to report some of the findings and document the design and implementation issues that have arisen and been tackled as prototyping work progresses.

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.

Enhancing Disability Determination Decision Process Through Natural Language Processing

2019 ◽  
Vol 4 (2) ◽  
pp. 15-28
Author(s):  
Eslam Amer
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Decision Process ◽  
Biomedical Text ◽  
Biomedical Text Mining ◽  
Disability Benefits ◽  
Disability Benefit ◽  
New Approach ◽  
Disability Determination

In this article, a new approach is introduced that makes use of the valuable information that can be extracted from a patient's electronic healthcare records (EHRs). The approach employs natural language processing and biomedical text mining to handle patient's data. The developed approach extracts relevant medical entities and builds relations between symptoms and other clinical signature modifiers. The extracted features are viewed as evaluation features. The approach utilizes such evaluation features to decide whether an applicant could gain disability benefits or not. Evaluations showed that the proposed approach accurately extracts symptoms and other laboratory marks with high F-measures (93.5-95.6%). Also, results showed an excellent deduction in assessments to approve or reject an applicant case to obtain a disability benefit.

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