LEARNING ENTITY AND WORD EMBEDDINGS FOR ENTITY DISAMBIGUATION

摘要

Technologies are described herein for learning entity and word embeddings for entity disambiguation. An example method includes pre-processing training data to generate one or more concurrence graphs of named entities, words, and document anchors extracted from the training data, defining a probabilistic model for the one or more concurrence graphs, defining an objective function based on the probabilistic model and the one or more concurrence graphs, and training at least one disambiguation model based on feature vectors generated through an optimized version of the objective function.