Paper Info
Title
A comparative study of Soft Computing software for enhancing the capabilities of business document management systems
Abstract
There are several types of business documents, ranging from brief accounting documents to complex legal agreements. Companies extensively use such documents to communicate, transact business and analyse their productivity. This results in the generation of a large number of documents daily, and small- and medium-sized enterprises are easily overwhelmed by this situation. Given this background, companies require software solutions which provide all of the features required by users for optimal document management, as well as optimising management processes and automating the extraction of relevant information from the documents. Open-source software provides these organizations with low-cost, high-quality software which incorporates an array of advanced features that extend beyond only storage solutions. In this study, we test several computational-intelligence open-source software tools in order to enhance the information-retrieval capabilities in small business document-management systems. We implement a prototype to test these Natural Language Processing (NLP) tools and Machine-Learning techniques in a business environment, with the aim of choosing the best alternative for each process.
Year
DOI
Venue
2016
10.1109/FUZZ-IEEE.2016.7737693
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Keywords
Field
DocType
business document management systems,accounting documents,complex legal agreements,small-and medium-sized enterprises,optimal document management,relevant information extraction,open-source software,low-cost high-quality software,computational-intelligence open-source software tools,information-retrieval capabilities,natural language processing tools,machine-learning techniques,business environment
Business software,Computer science,Enterprise software,Knowledge management,Software system,Artificial intelligence,Business process modeling,Software development,Business process management,Software engineering,Software as a service,Software quality,Machine learning
Conference
ISSN
ISBN
Citations 
1544-5615
978-1-5090-0627-4
0
PageRank 
References 
Authors
0.34
12
5
Name
Order
Citations
PageRank
Rubén Romero-Córdoba100.34
Francisco P. Romero223527.46
José Angel Olivas36512.87
Jesús Serrano-Guerrero4599.31
Arturo Peralta561.48