Entities are essential elements of natural language. In this paper, we
present methods for learning multi-level representations of entities on three
complementary levels: character (character patterns in entity names extracted,
e.g., by neural networks), word (embeddings of words in entity names) and
entity (entity embeddings). We investigate state-of-the-art learning methods on
each level and find large differences, e.g., for deep learning models,
traditional ngram features and the subword model of fasttext (Bojanowski et
al., 2016) on the character level; for word2vec (Mikolov et al., 2013) on the
word level; and for the order-aware model wang2vec (Ling et al., 2015a) on the
entity level. We confirm experimentally that each level of representation
contributes complementary information and a joint representation of all three
levels improves the existing embedding based baseline for fine-grained entity
typing by a large margin. Additionally, we show that adding information from
entity descriptions further improves multi-level representations of entities.

本文研究自然语言中实体的多层表示学习方法，包括字符级、单词级和实体级。我们比较了各级别上的现有学习方法，确认每个级别的表示都提供互补信息，同时提出将三个级别的表示联合起来可以大幅度提高细粒度实体类型识别的性能，而添加实体描述信息能够进一步增强多层次的实体表示。