  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.
