Experimental Evaluation of the Value of Structure: How to Efficiently Exploit Interdependencies in Sequence Labeling

Many problems in natural language processing, information extraction or bioinformatics consist in predicting a label for each element of a sequence of observations. The sequence of labels generally presents multiple dependencies that restrict the possible labels the elements can take. Therefore, relations between labels intuitively provide information valuable for the prediction. Several approaches have been proposed to take advantage of this additional information. However, experimental results show that taking relations into account does not always improve prediction performances, while it significantly increases the computational cost of both learning and prediction. In this work, we aim at both explaining these surprising results and proposing a simple but computationally efficient approach for labeling sequences.