Document (#42872)

Author

Lee, Y.-Y.
Ke, H.
Yen, T.-Y.
Huang, H.-H.
Chen, H.-H.

Title

Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement

Source

Journal of the Association for Information Science and Technology. 71(2020) no.6, S.657-670

Year

2020

Abstract

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.

Content

https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24289.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

Object

WordNet

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