Behavior-Informed Algorithms for Automatic Documentation Generation

2017 ◽  
Author(s):  
Paige Rodeghero
Keyword(s):  
Automatic Documentation

DARR: A Free-text Analysis System for the Automatic Documentation of Radiological Reports

1977 ◽  
Vol 16 (03) ◽  
pp. 144-153 ◽  
Author(s):  
E. Vaccari ◽  
W. Delaney ◽  
A. Chiesa
Keyword(s):  
Natural Language ◽  
Text Analysis ◽  
Automatic Documentation ◽  
Free Text ◽  
Software System ◽  
Automatic Translation ◽  
Radiological Report ◽  
Analysis System ◽  
Content Processing

A software system for the automatic free-text analysis and retrieval of radiological reports is presented. Such software involves: (1) automatic translation of the specific natural language in a formalized metalanguage in order to transform the radiological report in a »normalized report« analyzable by computer; (2) content processing of the normalized report to select desired information. The approach used to accomplish point (1) is described in detail referring to a specific application.

Carola, a computer system for automatic documentation in anesthesia

1987 ◽  
Vol 4 (4) ◽  
pp. 211-221 ◽  
Author(s):  
G. F. Karliczek ◽  
A. F. de Geus ◽  
G. Wiersma ◽  
S. Oosterhaven ◽  
I. Jenkins
Keyword(s):  
Computer System ◽  
Automatic Documentation

Semi-automatic documentation of an implemented linguistic grammar augmented with a treebank

2008 ◽  
Vol 42 (2) ◽  
pp. 117-126 ◽  
Author(s):  
Chikara Hashimoto ◽  
Francis Bond ◽  
Takaaki Tanaka ◽  
Melanie Siegel
Keyword(s):  
Automatic Documentation

scholarly journals REST API example generation using Javadoc

2017 ◽  
Vol 14 (2) ◽  
pp. 447-466
Author(s):  
Petri Rantanen
Keyword(s):  
Content Analysis ◽  
Web Services ◽  
Automatic Documentation ◽  
Media Content ◽  
Software Components ◽  
Media Content Analysis ◽  
Generating Method ◽  
Example Generation ◽  
Rest Api ◽  
Restful Web Services

Formatting and editing documentation can be a tedious process regardless of how well your documentation templates are made. Especially, keeping the code examples up-to-date can be time-consuming and error-prone. The research presented in this article describes a Javadoc extension that can be used to produce example data in combination with automatically generated API method call examples, and explains how the APIs in our implementation are organized to further ease the automatic documentation process. The primary goal is to make generating method call examples for (RESTful) web services easier. The method has been used in the implementation of a media content analysis service, and the experiences, advantages of using the described approach are discussed in this article. The method allows easier validation and maintenance for the documentation of method usage examples with a downside of an increased workload in the implementation of software components required for the automatic documentation process.

scholarly journals DACBench: A Benchmark Library for Dynamic Algorithm Configuration

2021 ◽  
Author(s):  
Theresa Eimer ◽  
André Biedenkapp ◽  
Maximilian Reimer ◽  
Steven Adriansen ◽  
Frank Hutter ◽  
...  
Keyword(s):  
Deep Learning ◽  
Evolutionary Computation ◽  
Automatic Documentation ◽  
Ai Planning ◽  
Dynamic Algorithm ◽  
Empirical Results ◽  
Broad Applicability ◽  
New Methods ◽  
Algorithm Configuration

Dynamic Algorithm Configuration (DAC) aims to dynamically control a target algorithm's hyperparameters in order to improve its performance. Several theoretical and empirical results have demonstrated the benefits of dynamically controlling hyperparameters in domains like evolutionary computation, AI Planning or deep learning. Replicating these results, as well as studying new methods for DAC, however, is difficult since existing benchmarks are often specialized and incompatible with the same interfaces. To facilitate benchmarking and thus research on DAC, we propose DACBench, a benchmark library that seeks to collect and standardize existing DAC benchmarks from different AI domains, as well as provide a template for new ones. For the design of DACBench, we focused on important desiderata, such as (i) flexibility, (ii) reproducibility, (iii) extensibility and (iv) automatic documentation and visualization. To show the potential, broad applicability and challenges of DAC, we explore how a set of six initial benchmarks compare in several dimensions of difficulty.

Sign in / Sign up

Export Citation Format

Share Document