2018/08/23 by Dimitri Kartsaklis, Kartsaklis, Dimitri, Mohammad Taher Pilehvar +3
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.AI #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.1808.07724
Accepted for presentation at EMNLP 2018 (main conference)
arxiv created 2018/08/23 · arxiv updated 2018/08/24
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.