Repository logo
 

Mapping text to knowledge graph entities using multi-sense LSTMs

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Kartsaklis, D 
Pilehvar, MT 

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.

Description

Keywords

Journal Title

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018

Conference Name

Empirical Methods in Natural Language Processing (EMNLP)

Journal ISSN

Volume Title

Publisher

Sponsorship
Engineering and Physical Sciences Research Council (EP/M005089/1)
Medical Research Council (MR/M025160/1)
NVidia Corporation for the donation of a Titan XP GPU