Name
Abstract | ||
|---|---|---|
An increasing number of approaches for ontology engineering from text are gearing towards the use of online sources such as company intranet and the World Wide Web. Despite such rise, not much work can be found in aspects of preprocessing and cleaning dirty texts from online sources. This paper presents an enhancement of an Integrated Scoring for Spelling error correction, Abbreviation expansion and Case restoration (ISSAC). ISSAC is implemented as part of a text preprocessing phase in an ontology engineering system. New evalua- tions performed on the enhanced ISSAC using 700 chat records reveal an improved accuracy of 98% as compared to 96.5% and 71% based on the use of only basic ISSAC and of Aspell, respectively. |
Year | Venue | Keywords |
|---|---|---|
2008 | Computing Research Repository | error correction,world wide web |
Field | DocType | Volume |
Data mining,Ontology engineering,Information retrieval,Computer science,Intranet,Error detection and correction,Preprocessor,Spelling,Artificial intelligence,Machine learning | Journal | abs/0810.0 |
ISSN | Citations | PageRank |
IJCAI Workshop on Analytics for Noisy Unstructured Text Data
(AND), 2007, pages 55-62 | 5 | 0.87 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wilson Wong | 1 | 144 | 9.77 |
| Wei Liu | 2 | 161 | 15.37 |
| Mohammed Bennamoun | 3 | 37 | 4.14 |