Bias term? about crfsuite

[kmike]

@usptact you can add a feature which is 1 for all training examples; that'd be a bias feature.
One difference is that usually bias is not regularized, but this feature will be regularized like other features. If that's important you can use values like 100 instead of 1, the effect will be similar to not regularizing bias.

[jlerouge]

@usptact Yes, the bias is important, especially if you have unbalanced classes. Mikhail already told you how to do it.

Independently to your initial question, I wonder what is the point of using a CRF to classify a single document ? You could use other frameworks more specifically designed for this purpose (neural networks, SVMs...).

[usptact]

@kmike @jlerouge Thank you guys for the input! I see now better the place of the bias term. The reason why I am doing this is CRFSuite is now an embedded piece of software in our NLP pipeline. We train many CRF models which do primarily NER but also Chinese segmentation as well as domain classification (classifying short documents). The advantage is that the same tool is used everywhere and it is very fast to train.

[usptact]

@jlerouge Another reason using linear logistic regression is that it perfectly suffices for the task. We found very little gain (if any) using non-linear techniques for short text classification. Short texts here are documents composed of 3-10 words.