Abstract
Linear-chain conditional random fields are a state-of-the-art machine learner for sequential labeling tasks. Altun investigated various loss functions for linear-chain conditional random fields. Tsuboi introduced smoothing method between point-wise loss function and sequential loss function. Sarawagi proposed semi-markov conditional random fields in which variable length of observed tokens are regarded as one node in lattice function. We propose a smoothing method among several loss functions for semi-markov conditional random fields. We draw a comparison among the loss functions and smoothing rate settings in base phrase chunking and named entity recognition tasks.
