Abstract
Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a low-dimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task.
Original language | English |
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Title of host publication | NAACL HLT 2016 - The 2016 Conference of the North American Chapter of the Association for Computational Linguistics |
Subtitle of host publication | Human Language Technologies, Proceedings of the Conference |
Editors | Ani Nenkova, Owen Rambow |
Place of Publication | Stroudsburg PA USA |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 460-466 |
Number of pages | 7 |
ISBN (Electronic) | 9781941643914 |
DOIs | |
Publication status | Published - 2016 |
Externally published | Yes |
Event | North American Association for Computational Linguistics 2016 - Sheraton San Diego Hotel & Marina, San Diego, United States of America Duration: 12 Jun 2016 → 17 Jun 2016 Conference number: 15th http://naacl.org/naacl-hlt-2016/ |
Conference
Conference | North American Association for Computational Linguistics 2016 |
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Abbreviated title | NAACL HLT 2016 |
Country/Territory | United States of America |
City | San Diego |
Period | 12/06/16 → 17/06/16 |
Internet address |