scholarly journals Automated Analysis of Natural Language Properties for UML Models

2006 ◽  
pp. 48-57 ◽  
Author(s):  
Sascha Konrad ◽  
Betty H. C. Cheng
Keyword(s):  
Natural Language ◽  
Automated Analysis ◽  
Uml Models

Robust methods in analysis of natural language data

2002 ◽  
Vol 8 (2-3) ◽  
pp. 93-96
Author(s):  
AFZAL BALLIM ◽  
VINCENZO PALLOTTA
Keyword(s):  
Information Systems ◽  
Natural Language ◽  
Automated Analysis ◽  
Approximate Analysis ◽  
Robust Methods ◽  
Analysis Techniques ◽  
Intelligent Information Systems ◽  
Recent Interest ◽  
Language Data ◽  
Intelligent Information

The automated analysis of natural language data has become a central issue in the design of intelligent information systems. Processing unconstrained natural language data is still considered as an AI-hard task. However, various analysis techniques have been proposed to address specific aspects of natural language. In particular, recent interest has been focused on providing approximate analysis techniques, assuming that when perfect analysis is not possible, partial results may be still very useful.

Online Critical Debate Model: Designing and Analyzing Deliberation for the Digital Age

2021 ◽  
pp. 69-86
Author(s):  
Claudio Fuentes Bravo ◽  
Julián Goñi Jerez
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Public Engagement ◽  
Large Scale ◽  
Digital Age ◽  
Automated Analysis ◽  
Online Version ◽  
Online Format

Through our experience during a large-scale public engagement exercise in Chile we draw conclusions to adapt and improve the Critical Debate Model to an online format. We highlight the importance of epistemic opposition and structured annotation for the execution of debates, while also exploring the possibilities of automated analysis using Natural Language Processing. We conclude by describing how an online version of the Critical Debate Model could be implemented.

Special Issue on Robust Methods in Analysis of Natural Language Data

2001 ◽  
Vol 7 (2) ◽  
pp. 189-190
Author(s):  
Afzal Ballim ◽  
Vincenzo Pallotta
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Automated Analysis ◽  
Robust Methods ◽  
Human Communication ◽  
Analysis Techniques ◽  
Spoken Language Processing ◽  
Intelligent Information Systems ◽  
Language Data ◽  
Intelligent Information

The automated analysis of natural language data has become a central issue in the design of Intelligent Information Systems. The term natural language is intended to cover all the possible modalities of human communication and it is not restricted to written or spoken language. Processing unrestricted natural language is still considered as an AI-hard task. However various analysis techniques have been proposed in order to address specific aspects of natural language. In particular, recent interest has been on providing approximate analysis techniques, assuming that perfect analysis is not possible, but that partial results are still very useful.

scholarly journals The promise of natural language processing in healthcare

2019 ◽  
Vol 87 (2) ◽  
pp. 21-23
Author(s):  
Rohin Attrey ◽  
Alexander Levit
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Healthcare Delivery ◽  
Daily Practice ◽  
Automated Analysis ◽  
Medical Support ◽  
Slowing Down ◽  
Large Sets

The healthcare industry generates data at a rapid rate, with no signs of slowing down. A large portion of this information takes the form of unstructured narrative text, making it difficult for computer systems to analyze the data in a usable format. However, automated analysis of this information could be incredibly useful in daily practice. This could be accomplished with natural language processing, an area of artificial intelligence and computational linguistics that is used to analyze and process large sets of unstructured data, namely spoken or written communication. Natural language processing has already been implemented in many sectors, and the industry is projected to be worth US$16 billion by 2021. Natural language processing could take unstructured patient data and interpret meaning from the text, allowing that information to inform healthcare delivery. Natural language processing can also enable intelligent chatbots, interacting and providing medical support to patients. It has the potential to aid physicians by efficiently summarizing patient charts and predicting patient outcomes. In hospitals, it has the ability to analyze patient satisfaction and facilitate quality improvement. Despite current technical limitations, natural language processing is a rapidly developing technology that promises to improve the quality and efficiency of healthcare delivery.

All work and no play: A text analysis

2019 ◽  
Vol 61 (3) ◽  
pp. 236-251 ◽  
Author(s):  
Kate Downer ◽  
Chrissie Wells ◽  
Charlotte Crichton
Keyword(s):  
Natural Language ◽  
Survey Data ◽  
Text Analysis ◽  
Market Research ◽  
Human Error ◽  
Automated Analysis ◽  
Text Analytics ◽  
Scalar Data ◽  
Over Time

Automated analysis of open-ended text survey data is an appealing prospect. It eliminates human error and human variability and can be used to create models that are easier to update over time than a manual approach to coding generally yields. Today, text analytics is a huge business and is among the most popular innovations within the current research landscape. However, within the research industry, there has been little change in usage in recent years, and awareness of the options available appears to be limited. We wished to look more closely at the true strengths of different approaches, the main barriers to their adoption, and how these might be overcome. Using text responses from a short survey about work and play in two markets, we contrasted two tools in analyzing the output: Q’s text analysis component and Google Cloud Natural Language. We chose these tools as they can each be easily applied to survey data but are based on different analytic principles. We found some surprising differences between the output of the two tools and between the text analysis metrics and scalar data. We conclude by discussing some of the key contemporary themes in text analytics and the likely future role of this method within market research and insight.

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