Fine-Grained Named Entity Classification with Wikipedia Article Vectors

Fine-Grained Named Entity Classification with Wikipedia Article Vectors - scientific work related to Wikipedia quality published in 2016, written by Masatoshi Suzuki, Koji Matsuda, Satoshi Sekine, Naoaki Okazaki and Kentaro Inui.

Overview

This paper addresses the task of assigning multiple labels of fine-grained named entity (NE) types to Wikipedia articles. To address the sparseness of the input feature space, which is salient particularly in fine-grained type classification, authors propose to learn article vectors (i.e. entity embeddings) from hypertext structure of Wikipedia using a Skip-gram model and incorporate them into the input feature set. To conduct large-scale practical experiments, authors created a new dataset containing over 22,000 manually labeled instances. The results of experiments show that idea gained statistically significant improvements in classification results.