A high accuracy method for semi-supervised information extraction
| Autor: | Antonio Sanfilippo, Stephen Tratz |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Active learning (machine learning)
Computer science business.industry Algorithmic learning theory Stability (learning theory) Online machine learning Semi-supervised learning computer.software_genre Machine learning User requirements document Robot learning Personalization Information extraction Computational learning theory Unsupervised learning Learning to rank Instance-based learning Data mining Artificial intelligence business computer |
| Zdroj: | HLT-NAACL (Short Papers) |
| Popis: | Customization to specific domains of discourse and/or user requirements is one of the greatest challenges for today's Information Extraction (IE) systems. While demonstrably effective, both rule-based and supervised machine learning approaches to IE customization pose too high a burden on the user. Semi-supervised learning approaches may in principle offer a more resource effective solution but are still insufficiently accurate to grant realistic application. We demonstrate that this limitation can be overcome by integrating fully-supervised learning techniques within a semi-supervised IE approach, without increasing resource requirements. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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