Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement
In this research, we propose 3 different approaches to measure the semantic relatedness between 2 words: (i) boost the performance of GloVe word embedding model via removing or transforming abnormal dimensions; (ii) linearly combine the information extracted from WordNet and word embeddings; and (iii) utilize word embedding and 12 linguistic information extracted from WordNet as features for Support Vector Regression. We conducted our experiments on 8 benchmark data sets, and computed Spearman correlations between the outputs of our methods and the ground truth. We report our results together with 3 state of the art approaches. The experimental results show that our method can outperform state of the art approaches in all the selected English benchmark data sets.
J. Assoc. Inf. Sci. Technol.