Name
Title | ||
|---|---|---|
Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation |
Abstract | ||
|---|---|---|
Data Selection has emerged as a common issue in language technologies. We define Data Selection as the choosing of a subset of training data that is most effective for a given task. This paper describes deductive feature detection, one component of a data selection system for machine translation. Feature detection determines whether features such as tense, number, and person are expressed in a language. The database of the The World Atlas of Language Structures provides a gold standard against which to evaluate feature detection. The discovered features can be used as input to a Navigator, which uses active learning to determine which piece of language data is the most important to acquire next. |
Year | Venue | Keywords |
|---|---|---|
2008 | SIXTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, LREC 2008 | feature detection,language technology,machine translation,active learning,gold standard |
Field | DocType | Citations |
Training set,Active learning,Feature detection,Data selection,Computer science,Machine translation,Speech recognition,Natural language processing,Artificial intelligence | Conference | 2 |
PageRank | References | Authors |
0.42 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan H. Clark | 1 | 411 | 16.42 |
| Robert E. Frederking | 2 | 356 | 63.82 |
| Lori S. Levin | 3 | 372 | 46.46 |