Name
Abstract | ||
|---|---|---|
Information systems are using an increasing amount of unstructured information in the form of text. This situation has spawned a need to improve the text-mining technologies needed for information retrieval, filtering, and classification. This article compares some of the options available and how they can provide textual data-mining functionalities to software applications. In particular, the authors focus on Pimiento, a new object-oriented application framework for text mining. This framework allows developers to easily create distributed applications that use machine learning and statistical techniques to automatically process documents. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/MIC.2006.85 | Internet Computing, IEEE |
Keywords | Field | DocType |
text-mining technology,software application,increasing amount,statistical technique,textual data-mining functionalities,computational linguistics,informa- tion extraction,software frameworks.,information retrieval,information system,new object-oriented application framework,summarization,text mining,mining text,categorization,clustering,unstructured information,statistical analysis,object oriented,object oriented programming,information classification,information extraction,data mining,software frameworks,machine learning,classification,information systems,learning artificial intelligence,text analysis,software framework,distributed application | Data science,Information system,World Wide Web,Information retrieval,Information technology,Computer science,Distributed algorithm,Information extraction,Knowledge engineering,Cluster analysis,Classified information,Software framework | Journal |
Volume | Issue | ISSN |
10 | 4 | 1089-7801 |
Citations | PageRank | References |
9 | 0.64 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. J. García Adeva | 1 | 23 | 2.31 |
| Rafael A. Calvo | 2 | 1033 | 91.13 |