Mapping Text to Knowledge Graph Entities with Multi-Sense LSTMs

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• Dimitri Kartsaklis (Apple)
• Thursday 21 February 2019, 11:00-12:00
• Faculty of English, Room SR24.

If you have a question about this talk, please contact Edoardo Maria Ponti.

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Abstract: In this talk we address 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.

Paper: http://aclweb.org/anthology/D18-1221 Code: https://bitbucket.org/dimkart/ms-lstm/

This talk is part of the Language Technology Lab Seminars series.

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