Sememes are minimum semantic units of concepts in human languages, such that
each word sense is composed of one or multiple sememes. Words are usually
manually annotated with their sememes by linguists, and form linguistic
common-sense knowledge bases widely used in various NLP tasks. Recently, the
lexical sememe prediction task has been introduced. It consists of
automatically recommending sememes for words, which is expected to improve
annotation efficiency and consistency. However, existing methods of lexical
sememe prediction typically rely on the external context of words to represent
the meaning, which usually fails to deal with low-frequency and
out-of-vocabulary words. To address this issue for Chinese, we propose a novel
framework to take advantage of both internal character information and external
context information of words. We experiment on HowNet, a Chinese sememe
knowledge base, and demonstrate that our framework outperforms state-of-the-art
baselines by a large margin, and maintains a robust performance even for
low-frequency words.

本研究提出一种结合内部字符信息和外部上下文信息的新型框架，用于汉语语言下的词汇语义预测任务，实验结果表明该方法可以提高语义标注的效率和一致性，对低频词仍然有较好的性能表现。