Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example, the need for labeled data can be drastically reduced by taking advantage of domain knowledge in the form of word lists, part-of-speech tags, character n-grams, and capitalization patterns. While it is difficult to capture such inter-dependent features with a generative probabilistic model, conditionally-trained models, such as conditional maximum entropy models, handle them well. There has been significant work with such models for greedy sequence modeling in NLP (Ratnaparkhi, 1996; Borthwick et al., 1998).
Early results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons
Published 2003 in Conference on Computational Natural Language Learning
ABSTRACT
PUBLICATION RECORD
- Publication year
2003
- Venue
Conference on Computational Natural Language Learning
- Publication date
2003-05-31
- Fields of study
Linguistics, Computer Science
- Identifiers
- External record
- Source metadata
Semantic Scholar
CITATION MAP
EXTRACTION MAP
CLAIMS
- No claims are published for this paper.
CONCEPTS
- No concepts are published for this paper.
REFERENCES
Showing 1-11 of 11 references · Page 1 of 1