Abstract
Mutual bootstrapping is a commonly used technique for many natural language processing tasks, including semantic lexicon induction. Among many bootstrapping methods, the Basilisk algorithm achieved successful applications through two key iterative steps: scoring context patterns and candidate instances. In this work, we improve Basilisk by modifying its two scoring functions. By incorporating AutoEncoder to the scoring functions of patterns and candidates, we can reduce the bias problems and obtain more balanced results. The experimental results demonstrate that our proposed methods for guiding bootstrapping of a semantic lexicon with AutoEncoder can boost overall performance.
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Acknowledgments
We would like to thank the anonymous reviewers for their helpful comments and suggestions. Chenlong Hu gratefully acknowledges the support from China Scholarship Council (CSC).
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Hu, C., Nakano, M., Okumura, M. (2019). AutoEncoder Guided Bootstrapping of Semantic Lexicon. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11672. Springer, Cham. https://doi.org/10.1007/978-3-030-29894-4_17
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DOI: https://doi.org/10.1007/978-3-030-29894-4_17
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