Abstract
The paper focuses on the manipulation of a WordNet-based knowledge graph by adding, changing and combining various semantic relations. This is done in the context of measuring similarity and relatedness between words, based on word embedding representations trained on a pseudo corpus generated from the knowledge graph. The UKB tool is used for generating pseudo corpora that are then used for learning word embeddings. The results from the performed experiments show that the addition of more relations generally improves performance along both dimensions – similarity and relatedness. In line with previous research, our survey confirms that paradigmatic relations predominantly improve similarity, while syntagmatic relations benefit relatedness scores.
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Notes
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Downloaded from http://alfonseca.org/eng/research/wordsim353.html.
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Downloaded from https://www.cl.cam.ac.uk/~fh295/simlex.html.
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The models are downloaded from here https://github.com/3Top/word2vec-api.
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Acknowledgements
This research has received partial support by the grant 02/12—Deep Models of Semantic Knowledge (DemoSem), funded by the Bulgarian National Science Fund in 2017–2019. We are grateful to the anonymous reviewers for their remarks, comments, and suggestions. All errors remain our own responsibility.
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Simov, K., Osenova, P., Popov, A. (2017). Comparison of Word Embeddings from Different Knowledge Graphs. In: Gracia, J., Bond, F., McCrae, J., Buitelaar, P., Chiarcos, C., Hellmann, S. (eds) Language, Data, and Knowledge. LDK 2017. Lecture Notes in Computer Science(), vol 10318. Springer, Cham. https://doi.org/10.1007/978-3-319-59888-8_19
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