Name
Title | ||
|---|---|---|
Instance pruning by filtering uninformative words: an information extraction case study |
Abstract | ||
|---|---|---|
In this paper we present a novel instance pruning technique for Information Extraction (IE). In particular, our technique filters out uninformative words from texts on the basis of the assumption that very frequent words in the language do not provide any specific information about the text in which they appear, therefore their expectation of being (part of) relevant entities is very low. The experiments on two benchmark datasets show that the computation time can be significantly reduced without any significant decrease in the prediction accuracy. We also report an improvement in accuracy for one task. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/978-3-540-30586-6_54 | CICLing |
Keywords | Field | DocType |
information extraction case study,technique filter,computation time,benchmark datasets,prediction accuracy,uninformative word,significant decrease,information extraction,relevant entity,novel instance pruning technique,specific information,frequent word | Pattern recognition,Computer science,Filter (signal processing),Information extraction,Specific-information,Natural language processing,Artificial intelligence,Very frequent,Machine learning,Word-sense disambiguation,Computation,Pruning | Conference |
Volume | ISSN | ISBN |
3406 | 0302-9743 | 3-540-24523-5 |
Citations | PageRank | References |
2 | 0.37 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alfio Massimiliano Gliozzo | 1 | 291 | 24.28 |
| Claudio Giuliano | 2 | 488 | 33.00 |
| Raffaella Rinaldi | 3 | 6 | 0.82 |