Overview of the NLPCC 2020 Shared Task: AutoIE

摘要

This is an overview paper of the NLPCC 2020 shared task on AutoIE, which aims to evaluate the information extraction solutions under low data resource. Given an unlabeled corpus, entity lists covering 30% entities in the corpus and some labeled validation samples, participants are required to build a named entity recognition system. There are 44 registered teams and 16 of them submitted results, the top system achieve 0.041 and 0.133 F1 score improvement upon the baseline system with or without labeled validation data respectively. The evaluation result indicates that it is possible to use less human annotation for information extraction system.