Unsupervised active learning of CRF model for cross-lingual named entity recognition

Abstract

Manual annotation of the training data of information extraction models is a time consuming and expensive process but necessary for the building of information extraction systems. Active learning has been proven to be effective in reducing manual annotation efforts for supervised learning tasks where a human judge is asked to annotate the most informative examples with respect to a given model. However, in most cases reliable human judges are not available for all languages. In this paper, we propose a cross-lingual unsupervised active learning paradigm (XLADA) that generates high-quality automatically annotated training data from a word-aligned parallel corpus. To evaluate our paradigm, we applied XLADA on English-French and English-Chinese bilingual corpora then we trained French and Chinese information extraction models. The experimental results show that XLADA can produce effective models without manually-annotated training data.