Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs
This addresses entity linking and classification for NLP applications, with incremental improvements in handling polysemy.
The paper tackles the problem of mapping natural language text to knowledge base entities by using a Multi-Sense LSTM with dynamic disambiguation and a knowledge graph enhanced with textual features, achieving state-of-the-art results on large-scale tasks.
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.