Name
Abstract | ||
|---|---|---|
Information Retrieval systems normally have to work with rather heterogeneous sources, such as Web sites or documents from Optical Character Recognition tools. The correct conversion of these sources into flat text files is not a trivial task since noise may easily be introduced as a result of spelling or typeset errors. Interestingly, this is not a great drawback when the size of the corpus is sufficiently large, since redundancy helps to overcome noise problems. However, noise becomes a serious problem in restricted-domain Information Retrieval specially when the corpus is small and has little or no redundancy. This paper devises an approach which adds noise-tolerance to Information Retrieval systems. A set of experiments carried out in the agricultural domain proves the effectiveness of the approach presented. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.datak.2013.02.002 | Data & Knowledge Engineering |
Keywords | Field | DocType |
Information Retrieval,Noise-tolerance,Restricted domain,Edit distance | Edit distance,Drawback,Data mining,Human–computer information retrieval,Information retrieval,Computer science,Optical character recognition,Redundancy (engineering),Noise tolerance,Database,Visual Word | Journal |
Volume | Issue | ISSN |
86 | 1 | 0169-023X |
Citations | PageRank | References |
0 | 0.34 | 33 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Katia Vila | 1 | 23 | 4.56 |
| Antonio Fernández Orquín | 2 | 11 | 1.75 |
| José M. Gómez | 3 | 32 | 8.02 |
| Antonio Ferrández | 4 | 226 | 25.57 |
| Josval Díaz | 5 | 6 | 1.14 |