Towards Building a Multilingual Sememe Knowledge Base: Predicting Sememes for BabelNet Synsets Fanchao Qi1∗, Liang Chang2∗y, Maosong Sun13z, Sicong Ouyang2y, Zhiyuan Liu1 1Department of Computer Science and Technology, Tsinghua University Institute for Artificial Intelligence, Tsinghua University Beijing National Research Center for Information Science and Technology 2Beijing University of Posts and Telecommunications 3Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University
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[email protected] Abstract word husband A sememe is defined as the minimum semantic unit of human languages. Sememe knowledge bases (KBs), which contain sense "married man" "carefully use" words annotated with sememes, have been successfully ap- plied to many NLP tasks. However, existing sememe KBs are built on only a few languages, which hinders their widespread human economize utilization. To address the issue, we propose to build a uni- sememe fied sememe KB for multiple languages based on BabelNet, a family male spouse multilingual encyclopedic dictionary. We first build a dataset serving as the seed of the multilingual sememe KB. It man- ually annotates sememes for over 15 thousand synsets (the entries of BabelNet). Then, we present a novel task of auto- Figure 1: Sememe annotation of the word “husband” in matic sememe prediction for synsets, aiming to expand the HowNet. seed dataset into a usable KB. We also propose two simple and effective models, which exploit different information of synsets. Finally, we conduct quantitative and qualitative anal- sememes to annotate senses of over 100 thousand Chinese yses to explore important factors and difficulties in the task.