'bias' feature in example

I’m very new to CRF so I apologize if my issue is just ignorance…

I was going through the example and noticed that word2features() added a 'bias' to the beginning of each feature set. Does this have a purpose? It seems that since every set of features will contain that ‘bias’ string that the end result should be the same without it. (or I’m just totally not getting it) I tried looking through the docs here and the crfsuite docs and couldn’t find anything that would indicate the purpose.

[Pantamis]

'bias' is a feature that capture the proportion of a given label in the training set.

Intuitively, if you have no other feature than 'bias' in your model (so your features are just indicator functions of the current label) then the weight of the feature learned will be higher if the label appears more. When you will predict, you will just always return the label with higher weight, which is the one which appear the most during training.

In a ‘real’ CRF, it is just a way to express that some label are rare by themself and other not, so you may take count of this (for example you can imagine a language in which verbs are mostly avoided and not nouns, so you should express that with the weight of the 'bias' feature lower for verbs labels than nouns).

I hope it is clear…

[hanifabd]

I applied it for sentence boundary disambiguation, when i remove the bias, my model be more aggresive in segmenting the sentence but when i add bias = 1 it make more better, but i didnt know the reason why