Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs

Dimitri Kartsaklis, Mohammad Taher Pilehvar, Nigel Collier


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.
Anthology ID:
D18-1221
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1959–1970
Language:
URL:
https://aclanthology.org/D18-1221
DOI:
10.18653/v1/D18-1221
Bibkey:
Cite (ACL):
Dimitri Kartsaklis, Mohammad Taher Pilehvar, and Nigel Collier. 2018. Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1959–1970, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs (Kartsaklis et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1221.pdf
Data
Cora