Fine Grained Named Entity Classification with Wikipedia Article Vectors
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, we 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, we created a new dataset containing over 22,000 manually labeled instances. The results of our experiments show that our idea gained statistically significant improvements in classification results.
2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)