Abstract
This paper describes the effect of introducing embedding-based features in a learning to rank approach to entity relatedness. We define several features that exploit word- and link-embedding approaches by relying on both links and the content that appear in Wikipedia articles. These features are combined with other state-of-the-art relatedness measures by using a learning to rank framework. In the evaluation, we report the performance of each feature individually. Moreover, we investigate the contribution of each feature to the ranking function by analysing the output of a feature selection algorithm. The results of this analysis prove that features based on word and link embeddings are able to increase the performance of the learning to rank algorithm.
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Notes
- 1.
Words that appear less than min-count are discarded.
- 2.
Available on line: https://dkpro.github.io/dkpro-jwpl/.
- 3.
Available on-line: https://sourceforge.net/p/lemur/wiki/RankLib/.
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Acknowledgments
This work is supported by the IBM Faculty Award “Deep Learning to boost Cognitive Question Answering” and the project “Multilingual Entity Liking” funded by the Apulia Region under the program FutureInResearch. The Titan X GPU used for this research was donated by the NVIDIA Corporation.
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Basile, P., Caputo, A., Rossiello, G., Semeraro, G. (2016). Learning to Rank Entity Relatedness Through Embedding-Based Features. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2016. Lecture Notes in Computer Science(), vol 9612. Springer, Cham. https://doi.org/10.1007/978-3-319-41754-7_51
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DOI: https://doi.org/10.1007/978-3-319-41754-7_51
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