Adapting the Iawa List of Microscopic Features for Hardwood Identification to Delta

IAWA Journal ◽  
1991 ◽  
Vol 12 (1) ◽  
pp. 34-50 ◽  
Author(s):  
Narcisana Espinoza de Pernia ◽  
Regis B. Miller
Keyword(s):  
Natural Language ◽  
Species Identification ◽  
Computer Programs ◽  
Word Processor ◽  
Wood Identification ◽  
Description Language ◽  
Taxonomic Descriptions ◽  
Interactive Species ◽  
Microscopic Features

The IAWA List of Microscopic Features for Hardwood Identification was adapted to DELTA (DEscription Language for TAxonomy), a package of computer programs for generating taxonomic descriptions and interactive species identification. The quality of our natural language descriptions generated by DELTA are suitable to prepare a first-draft manuscript. In specific taxon descriptions, minor changes to wording and syntax are more easily accomplished with a word processor, but all taxon changes to format, syntax, and wording are best accomplished with DELTA. As the user becomes more familiar with DELTA, the descriptions become more refined and fewer changes are necessary. The highly sophisticated interactive identification (INTKEY) program is flexible and versatile with many options to meet the needs of wood anatomists engaged in wood identification.

A WORD-BASED CHINESE LANGUAGE UNDERSTANDING SYSTEM

1988 ◽  
Vol 02 (01) ◽  
pp. 25-35
Author(s):  
TIAN-SHUN YAO
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Chinese Language ◽  
Computer Programs ◽  
World Knowledge ◽  
Knowledge Source ◽  
Language Understanding ◽  
Language Analysis ◽  
The World

With the word-based theory of natural language processing, a word-based Chinese language understanding system has been developed. In the light of psychological language analysis and the features of the Chinese language, this theory of natural language processing is presented with the description of the computer programs based on it. The heart of the system is to define a Total Information Dictionary and the World Knowledge Source used in the system. The purpose of this research is to develop a system which can understand not only Chinese sentences but also the whole text.

scholarly journals Sentiment of App with Word Vectors

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2156-2159
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Text Data ◽  
Vector Representations ◽  
Text Sentiment Analysis

Vector representations for language have been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Sentiment Analysis. In particular, we target three sub-tasks namely sentiment words extraction, polarity of sentiment words detection, and text sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. Vector representations has been used to compute various vector-based features and conduct systematically experiments to demonstrate their effectiveness. Using simple vector based features can achieve better results for text sentiment analysis of APP.

From Natural Language to Programming Language

2018 ◽  
pp. 110-130 ◽  
Author(s):  
Xiao Liu ◽  
Dinghao Wu
Keyword(s):  
Problem Solving ◽  
Software Development ◽  
Natural Language ◽  
Case Studies ◽  
Programming Language ◽  
Program Synthesis ◽  
Computer Programs

Programming remains a dark art for beginners or even professional programmers. Experience indicates that one of the first barriers for learning a new programming language is the rigid and unnatural syntax and semantics. After analysis of research on the language features used by non-programmers in describing problem solving, the authors propose a new program synthesis framework, dialog-based programming, which interprets natural language descriptions into computer programs without forcing the input formats. In this chapter, they describe three case studies that demonstrate the functionalities of this program synthesis framework and show how natural language alleviates challenges for novice programmers to conduct software development, scripting, and verification.

Understanding and Assessing Quality of Models and Modeling Languages

2019 ◽  
pp. 967-980
Author(s):  
John Krogstie
Keyword(s):  
Natural Language ◽  
Modeling Languages ◽  
Time And Space ◽  
One Dimensional ◽  
Human Conduct ◽  
Organizational Activity ◽  
Important Challenge ◽  
Forms Of Knowledge ◽  
Models And Modeling

An important challenge for organizational activity is to effectively represent and transfer knowledge. One reason why humans have excelled as a species is our ability to create common stories and represent, reuse, and transfer this as knowledge across time and space. Whereas in most areas of human conduct one-dimensional natural language texts are the main way of expressing and sharing knowledge, the authors see the need for and use of two and many-dimensional forms of knowledge representational to be on the rise. This will also enable users to capture contextual dependencies between roles, tasks, information elements, and the views required for performing work without having to go through traditional systems developers to have enhanced support for their work. The importance on supporting judgment on the quality of these models will thus increase along with the usefulness of frameworks for quality of models and modeling languages such as SEQUAL.

Blog Snippets Based Drug Effects Extraction System Using Lexical and Grammatical Restrictions

2014 ◽  
Vol 5 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Shiho Kitajima ◽  
Rafal Rzepka ◽  
Kenji Araki
Keyword(s):  
Quality Of Life ◽  
Natural Language ◽  
Large Scale ◽  
Medical Information ◽  
Drug Effects ◽  
Large Scale Data ◽  
Medical Documents ◽  
Baseline System ◽  
Scale Data

Obtaining medical information has a beneficial influence on patients' treatment and QOL (quality of life). The authors aim to make a system that helps patients to collect narrative information. Extracting information from data written by patients will allow the acquisition of information which is easy to understand and provides encouragement. Additionally, by using large-scale data, the system can be utilized for discovering unknown effects or patterns. As the first step, the purpose of this paper is to extract descriptions of the effects caused by taking drugs as a triplet of expressions from illness survival blogs' snippets. This paper proposes a method to extract the triplets using specific clue words and parsing the results in order to extract from blogs written in free natural language. Moreover, recall was improved by combining their proposed method and a baseline system, and precision was improved by filtering using dictionaries we created from existing medical documents.

scholarly journals Automatic Identification of Information Quality Metrics in Health News Stories

2020 ◽  
Vol 8 ◽  
Author(s):  
Majed Al-Jefri ◽  
Roger Evans ◽  
Joon Lee ◽  
Pietro Ghezzi
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Quality ◽  
Evaluation Process ◽  
Health News ◽  
News Stories

Objective: Many online and printed media publish health news of questionable trustworthiness and it may be difficult for laypersons to determine the information quality of such articles. The purpose of this work was to propose a methodology for the automatic assessment of the quality of health-related news stories using natural language processing and machine learning.Materials and Methods: We used a database from the website HealthNewsReview.org that aims to improve the public dialogue about health care. HealthNewsReview.org developed a set of criteria to critically analyze health care interventions' claims. In this work, we attempt to automate the evaluation process by identifying the indicators of those criteria using natural language processing-based machine learning on a corpus of more than 1,300 news stories. We explored features ranging from simple n-grams to more advanced linguistic features and optimized the feature selection for each task. Additionally, we experimented with the use of pre-trained natural language model BERT.Results: For some criteria, such as mention of costs, benefits, harms, and “disease-mongering,” the evaluation results were promising with an F1 measure reaching 81.94%, while for others the results were less satisfactory due to the dataset size, the need of external knowledge, or the subjectivity in the evaluation process.Conclusion: These used criteria are more challenging than those addressed by previous work, and our aim was to investigate how much more difficult the machine learning task was, and how and why it varied between criteria. For some criteria, the obtained results were promising; however, automated evaluation of the other criteria may not yet replace the manual evaluation process where human experts interpret text senses and make use of external knowledge in their assessment.

Identifying Heart Failure Symptoms and Poor Self-Management in Home Healthcare: A Natural Language Processing Study

2021 ◽  
Author(s):  
Sena Chae ◽  
Jiyoun Song ◽  
Marietta Ojo ◽  
Maxim Topaz
Keyword(s):  
Heart Failure ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Symptom Management ◽  
Home Healthcare ◽  
Self Management ◽  
Clinical Notes ◽  
Patients With Heart Failure

The goal of this natural language processing (NLP) study was to identify patients in home healthcare with heart failure symptoms and poor self-management (SM). The preliminary lists of symptoms and poor SM status were identified, NLP algorithms were used to refine the lists, and NLP performance was evaluated using 2.3 million home healthcare clinical notes. The overall precision to identify patients with heart failure symptoms and poor SM status was 0.86. The feasibility of methods was demonstrated to identify patients with heart failure symptoms and poor SM documented in home healthcare notes. This study facilitates utilizing key symptom information and patients’ SM status from unstructured data in electronic health records. The results of this study can be applied to better individualize symptom management to support heart failure patients’ quality-of-life.

scholarly journals Automated methods for the comparison of natural languages

2020 ◽  
Vol 23 (1-4) ◽  
Author(s):  
Gabriel Wittum ◽  
Michael Hoffer ◽  
Babett Lemke ◽  
Robert Jabs ◽  
Arne Nägel
Keyword(s):  
Structural Analysis ◽  
Natural Language ◽  
Native Language ◽  
General Question ◽  
Mathematical Language ◽  
Natural Languages ◽  
Set Up ◽  
Simple Sentences ◽  
Automated Methods

AbstractStarting from the general question, if there is a connection between the mathematical capabilities of a student and his native language, we aim at comparing natural languages with mathematical language quantitatively. In [20] we set up an approach to compare language structures using Natural Language Processors (NLP). However, difficulties arose with the quality of the structural analysis of the NLP used just comparing simple sentences in different but closely related natural languages. We now present a comparison of different available NLPs and discuss the results. The comparison confirms the results from [20], showing that current NLPs are not capable of analysing even simple sentences such that resulting structures between different natural languages can be compared.

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