A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance

Understand

The need to measure sequence similarity arises in information extraction, object identity, data mining, biological sequence analysis, and other domains.

• This paper presents discriminative string-edit CRFs, a finitestate conditional random field model for edit sequences between strings.
• Conditional random fields have advantages over generative approaches to this problem, such as pair HMMs or the work of Ristad and Yianilos, because as conditionally-trained methods, they enable the use of complex, arbitrary actions and features of the input strings.
• As in generative models, the training data does not have to specify the edit sequences between the given string pairs.