Active Learning with Subsequence Sampling Strategy for Sequence Labeling Tasks

Abstract

We propose an active learning framework for sequence labeling tasks. In each iteration, a set of subsequences are selected and manually labeled, while the other parts of sequences are left unannotated. The learning will stop automatically when the training data between consecutive iterations does not significantly change. We evaluate the proposed framework on chunking and named entity recognition data provided by CoNLL. Experimental results show that we succeed in obtaining the supervised F₁ only with 6.98%, and 7.01% of tokens being annotated, respectively.