scholarly journals Analyzing Machine‐Learned Representations: A Natural Language Case Study

2020 ◽  
Vol 44 (12) ◽  
Author(s):  
Ishita Dasgupta ◽  
Demi Guo ◽  
Samuel J. Gershman ◽  
Noah D. Goodman
Keyword(s):  
Natural Language

scholarly journals On systematically building a controlled natural language for functional requirements

2021 ◽  
Vol 26 (4) ◽  
Author(s):  
Alvaro Veizaga ◽  
Mauricio Alferez ◽  
Damiano Torre ◽  
Mehrdad Sabetzadeh ◽  
Lionel Briand
Keyword(s):  
Information System ◽  
Natural Language ◽  
Qualitative Methodology ◽  
Case Study Research ◽  
Empirical Evaluation ◽  
Functional Requirements ◽  
Industrial Case Study ◽  
Requirements Specifications ◽  
Financial Domain

AbstractNatural language (NL) is pervasive in software requirements specifications (SRSs). However, despite its popularity and widespread use, NL is highly prone to quality issues such as vagueness, ambiguity, and incompleteness. Controlled natural languages (CNLs) have been proposed as a way to prevent quality problems in requirements documents, while maintaining the flexibility to write and communicate requirements in an intuitive and universally understood manner. In collaboration with an industrial partner from the financial domain, we systematically develop and evaluate a CNL, named Rimay, intended at helping analysts write functional requirements. We rely on Grounded Theory for building Rimay and follow well-known guidelines for conducting and reporting industrial case study research. Our main contributions are: (1) a qualitative methodology to systematically define a CNL for functional requirements; this methodology is intended to be general for use across information-system domains, (2) a CNL grammar to represent functional requirements; this grammar is derived from our experience in the financial domain, but should be applicable, possibly with adaptations, to other information-system domains, and (3) an empirical evaluation of our CNL (Rimay) through an industrial case study. Our contributions draw on 15 representative SRSs, collectively containing 3215 NL requirements statements from the financial domain. Our evaluation shows that Rimay is expressive enough to capture, on average, 88% (405 out of 460) of the NL requirements statements in four previously unseen SRSs from the financial domain.

scholarly journals Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder

2020 ◽  
Vol 20 (S11) ◽  
Author(s):  
Jacqueline Peng ◽  
Mengge Zhao ◽  
James Havrilla ◽  
Cong Liu ◽  
Chunhua Weng ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Manifestations ◽  
Predictive Performance ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Research Articles

Abstract Background Natural language processing (NLP) tools can facilitate the extraction of biomedical concepts from unstructured free texts, such as research articles or clinical notes. The NLP software tools CLAMP, cTAKES, and MetaMap are among the most widely used tools to extract biomedical concept entities. However, their performance in extracting disease-specific terminology from literature has not been compared extensively, especially for complex neuropsychiatric disorders with a diverse set of phenotypic and clinical manifestations. Methods We comparatively evaluated these NLP tools using autism spectrum disorder (ASD) as a case study. We collected 827 ASD-related terms based on previous literature as the benchmark list for performance evaluation. Then, we applied CLAMP, cTAKES, and MetaMap on 544 full-text articles and 20,408 abstracts from PubMed to extract ASD-related terms. We evaluated the predictive performance using precision, recall, and F1 score. Results We found that CLAMP has the best performance in terms of F1 score followed by cTAKES and then MetaMap. Our results show that CLAMP has much higher precision than cTAKES and MetaMap, while cTAKES and MetaMap have higher recall than CLAMP. Conclusion The analysis protocols used in this study can be applied to other neuropsychiatric or neurodevelopmental disorders that lack well-defined terminology sets to describe their phenotypic presentations.

The Role of Cognitive Patterns in Modelling Reality by Means of Natural Language (The Case Study of the English Cognitive Pattern “Cleanness / Dirtiness”)

2021 ◽  
pp. 69-81
Author(s):  
Ekaterina Savitskaya ◽  
Keyword(s):  
Natural Language ◽  
Cognitive Linguistics ◽  
Human Mind ◽  
Specific Language ◽  
Cognitive Patterns ◽  
Cognitive Pattern ◽  
The World ◽  
Internal Comparison

In the field of cognitive linguistics it is accepted that, before developing its capacity for abstract and theoretical thought, the human mind went through the stage of reflecting reality through concrete images and thus has inherited old cognitive patterns. Even abstract notions of the modern civilization are based on traditional concrete images, and it is all fixed in natural language units. By way of illustration, the author analyzes the cognitive pattern “сleanness / dirtiness” as a constituent part of the English linguoculture, looking at the whole range of its verbal realization and demonstrating its influence on language-based thinking and modeling of reality. Comparing meanings of language units with their inner forms enabled the author to establish the connection between abstract notions and concrete images within cognitive patterns. Using the method of internal comparison and applying the results of etymological reconstruction of language units’ inner form made it possible to see how the world is viewed by representatives of the English linguoculture. Apparently, in the English linguoculture images of cleanness / dirtiness symbolize mainly two thematic areas: that of morality and that of renewal. Since every ethnic group has its own axiological dominants (key values) that determine the expressiveness of verbal invectives, one can draw the conclusion that people perceive and comprehend world fragments through the prism of mental stereo-types fixed in the inner form of language units. Sometimes, in relation to specific language units, a conflict arises between the inner form which retains traditional thinking and a meaning that reflects modern reality. Still, linguoculture is a constantly evolving entity, and its de-velopment entails breaking established stereotypes and creating new ones. Linguistically, the victory of the new over the old is manifested in the “dying out” of the verbal support for pre-vious cognitive patterns, which leads to “reprogramming” (“recoding”) of linguoculture rep-resentatives’ mentality.

scholarly journals Natural Language Processing (NLP) and Its Impact across Industries – Unlocking the True Potential of Digital Healthcare (A Case Study Approach)

2021 ◽  
pp. 86-98
Author(s):  
Sourajit Roy ◽  
Pankaj Pathak ◽  
S. Nithya
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computational Models ◽  
M2m Communication ◽  
Digital Revolution ◽  
Language Complexity ◽  
Study Approach ◽  
Data Variability

During the advent of the 21st century, technical breakthroughs and developments took place. Natural Language Processing or NLP is one of their promising disciplines that has been increasingly dynamic via groundbreaking findings on most computer networks. Because of the digital revolution the amounts of data generated by M2M communication across devices and platforms such as Amazon Alexa, Apple Siri, Microsoft Cortana, etc. were significantly increased. This causes a great deal of unstructured data to be processed that does not fit in with standard computational models. In addition, the increasing problems of language complexity, data variability and voice ambiguity make implementing models increasingly harder. The current study provides an overview of the potential and breadth of the NLP market and its acceptance in industry-wide, in particular after Covid-19. It also gives a macroscopic picture of progress in natural language processing research, development and implementation.

From Textual Scenarios to Message Sequence Charts

2010 ◽  
pp. 83-105 ◽  
Author(s):  
Leonid Kof
Keyword(s):  
Natural Language ◽  
Computational Linguistics ◽  
Development Project ◽  
Message Sequence Charts ◽  
Software Development Project ◽  
Requirement Document ◽  
Requirements Documents ◽  
Message Sequence Chart

Requirements engineering, the first phase of any software development project, is the Achilles’ heel of the whole development process, as requirements documents are often inconsistent and incomplete. In industrial requirements documents natural language is the main presentation means. In such documents, the system behavior is specified in the form of use cases and their scenarios, written as a sequence of sentences in natural language. For the authors of requirements documents some facts are so obvious that they forget to mention them. This surely causes problems for the requirements analyst. By the very nature of omissions, they are difficult to detect by document reviews: Facts that are too obvious to be written down at the time of document writing, mostly remain obvious at the time of review. In such a way, omissions stay undetected. This book chapter presents an approach that analyzes textual scenarios with the means of computational linguistics, identifies where actors or whole actions are missing from the text, completes the missing information, and creates a message sequence chart (MSC) including the information missing from the textual scenario. Finally, this MSC is presented to the requirements analyst for validation. The book chapter presents also a case study where scenarios from a requirement document based on industrial specifications were translated to MSCs. The case study shows feasibility of the approach.

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