Mapping text to knowledge graph entities using multi-sense LSTMs
Authors
Kartsaklis, D
Pilehvar, MT
Collier, N
Publication Date
2018Journal Title
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
Conference Name
Empirical Methods in Natural Language Processing (EMNLP)
ISBN
9781948087841
Pages
1959-1970
Type
Conference Object
This Version
AM
Metadata
Show full item recordCitation
Kartsaklis, D., Pilehvar, M., & Collier, N. (2018). Mapping text to knowledge graph entities using multi-sense LSTMs. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, 1959-1970. https://doi.org/10.17863/CAM.35220
Abstract
This paper addresses the problem of mapping natural language text to knowledge base entities. The mapping process is approached as a composition of a phrase or a sentence into a point in a multi-dimensional entity space obtained from a knowledge graph. The compositional model is an LSTM equipped with a dynamic disambiguation mechanism on the input word embeddings (a Multi-Sense LSTM), addressing polysemy issues. Further, the knowledge base space is prepared by collecting random walks from a graph enhanced with textual features, which act as a set of semantic bridges between text and knowledge base entities. The ideas of this work are demonstrated on large-scale text-to-entity mapping and entity classification tasks, with state of the art results.
Sponsorship
NVidia Corporation for the donation of a Titan XP GPU
Funder references
Engineering and Physical Sciences Research Council (EP/M005089/1)
Medical Research Council (MR/M025160/1)
Identifiers
External DOI: https://doi.org/10.17863/CAM.35220
This record's URL: https://www.repository.cam.ac.uk/handle/1810/287907
Rights
Licence:
http://www.rioxx.net/licenses/all-rights-reserved
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