Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings
Shoma Yamaki, Hiroyuki Shinnou, Kanako Komiya, Minoru Sasaki · Institutional Repositories DataBase (IRDB) · 2016
In this paper, we propose a method that employs sentences similarities from context word embeddings for supervised word sense disambiguation.In particular, if N example sentences exist in training data, an N-dimensional vector with N similarities between each pair of example sentences is added to a basic feature vector.This new feature vector is used to train a classifier and identification.We evaluated the proposed method using the feature vectors based on Bag-of-Words, SemEval-2 baseline as basic feature vectors and SemEval-2 Japanese task.The experimental results suggest that the method is more effective than the method with only basic vectors.