Efficient Inference in Large Conditional Random Fields

By Trevor Cohn. 2006

35 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods, Information Extraction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Charles Sutton
2. Andrew McCallum
3. François Yvon
4. Thomas Lavergne
5. Olivier Capp'e
6. Minwoo Jeong
7. G. G. Lee
8. Percy Liang

In Probabilistic & Bayesian Methods

1. Andrew McCallum
2. Minwoo Jeong
3. Olivier Capp'e
4. François Yvon
5. G. G. Lee
6. Charles Sutton
7. Pedro M. Domingos
8. Hoifung Poon

In Information Extraction

1. Charles Sutton
2. Andrew McCallum
3. Minwoo Jeong
4. G. G. Lee
5. Liang Huang
6. David Chiang
7. Ronan Collobert
8. Lluís Màrquez i Villodre

Citations in the corpus

• Practical Very Large Scale CRFs (2010, background): “Related ideas are discussed by Dietterich et al. (2004); by Cohn (2006), who considers “generalized” feature functions; and by Jeong et al. (2009), who use approximations to simplify the forward-backward recursions.”
• Multi-domain spoken language understanding with transfer learning (2009, methodology): “To alleviate this problem, we can use approximate inferences (Cohn, 2006; Jeong and Lee, 2008) and the tractable learning technique (Sutton and McCallum, 2007).”
• An Introduction to Conditional Random Fields (2010, background): “A related trick, that will also speed up forward backward, is to tie the parameters for certain subsets of transitions [20].”