Effective Crowdsourcing of Multiple Tasks for Comprehensive Information Extraction

<b>Introduction</b>This dataset aims to propose a Korean information extraction standard and promote research in this field by presenting crowdsourcing data collected for four information extraction tasks from the same corpus and the training and evaluation results for each task of a state-of-the-art model. These machine learning data for Korean information extraction are the first of their kind, and there are plans to continuously increase the data volume. The test results will serve as a standard for each Korean information extraction task and are expected to serve as a comparison target for various studies on Korean information extraction using the data collected in this study. The dataset is available for research purposes.<br><br><b>Description</b> - There are two crowdsourcing .zip files; wiki-10000-part1&amp;2.zip. In each file, 1) task1-1 : Entity Detection2) task1-2 : Entity Linking3) task2 : co-reference resolution4) task4 : relation extraction<b><br></b> - For an entity linking model(https://github.com/machinereading/eld-2018), here is a pre-trained embedding files in el-korean.tar.gz<br>- For an co-reference resolution model(https://github.com/machinereading/CR), here is a pre-trained embedding files in cr-korean.tar.gz<br><br>- For a relation extraction model(https://github.com/machinereading/re-gan), here is a corpus, dataset and pre-trained embedding files in ko-gan-data.zip<br><br>- For a relation extraction model(https://github.com/machinereading/re-re-RL-Crowd), here is a pre-trained embedding files in rerl-korean.tar.gz<br><br><b>How to use</b>All crowdsourcing file are in JSON format. Detail example and usage are in here (https://github.com/machinereading/okbqa-7-task4)