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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities
%A Yaghoobzadeh, Yadollah
%A Schütze, Hinrich
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F yaghoobzadeh-schutze-2017-multi
%X 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.
%U https://aclanthology.org/E17-1055
%P 578-589

Markdown (Informal)

[Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities](https://aclanthology.org/E17-1055) (Yaghoobzadeh & Schütze, EACL 2017)

• Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities (Yaghoobzadeh & Schütze, EACL 2017)

ACL

• Yadollah Yaghoobzadeh and Hinrich Schütze. 2017. Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 578–589, Valencia, Spain. Association for Computational Linguistics.