Coupled Semi-Supervised Learning for Chinese Knowledge Extraction
| Autor: | Lee-Heng Ma, 麻立恒 |
|---|---|
| Rok vydání: | 2013 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 101 Robust intelligent applications benefit from rich knowledge bases. Building a rich and complete knowledge base is a time-comsuming and labor-intensive task. Never Ending Language Learning (NELL) is a great demonstration for large-scale automatic knowledge extraction, but unfortunately some components in NELL are not suitable to deal with Chinese. This thesis presents a Coupled Chinese Pattern Learner (CCPL), which extracts knowledge by textual patterns on relationships between nouns and verbs in Chinese sentences. We also implement Coupled Set Expander for Any Language (CSEAL) to collaborate with CCPL. The experiments show our system is capable of large-scale learning, and preserves high accuracy in automatic extraction for Chinese knowledge. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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