%0 Conference Proceedings %T Entity Linking via Joint Encoding of Types, Descriptions, and Context %A Gupta, Nitish %A Singh, Sameer %A Roth, Dan %Y Palmer, Martha %Y Hwa, Rebecca %Y Riedel, Sebastian %S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing %D 2017 %8 September %I Association for Computational Linguistics %C Copenhagen, Denmark %F gupta-etal-2017-entity %X For accurate entity linking, we need to capture various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge. Additionally, a linking system should work on texts from different domains without requiring domain-specific training data or hand-engineered features. In this work we present a neural, modular entity linking system that learns a unified dense representation for each entity using multiple sources of information, such as its description, contexts around its mentions, and its fine-grained types. We show that the resulting entity linking system is effective at combining these sources, and performs competitively, sometimes out-performing current state-of-the-art systems across datasets, without requiring any domain-specific training data or hand-engineered features. We also show that our model can effectively “embed” entities that are new to the KB, and is able to link its mentions accurately. %R 10.18653/v1/D17-1284 %U https://aclanthology.org/D17-1284 %U https://doi.org/10.18653/v1/D17-1284 %P 2681-2690