Active learning has proven to be a successful strategy in quick development of corpora to be used in statistical induction of natural language. A vast majority of studies in this field has concentrated on finding and testing various informativeness measures for samples / however, representativeness measures for samples have not been thoroughly studied. In this thesis, we introduce a novel representativeness measure which is, being based on Zipf' / s law, model-independent and validated both theoretically and empirically. Experiments conducted on WSJ corpus with a wide-coverage parser show that our representativeness measure leads to better performance than previously introduced representativeness measures when used with most of the known informativeness measures.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/3/12609684/index.pdf |
| Date | 01 June 2008 |
| Creators | Cobanoglu, Onur |
| Contributors | Bozsahin, Huseyin Cem |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
Page generated in 0.0024 seconds