KDSL: a Knowledge-Driven Supervised Learning Framework for Word Sense Disambiguation

@article{Yin2019KDSLAK,
  title={KDSL: a Knowledge-Driven Supervised Learning Framework for Word Sense Disambiguation},
  author={Shi Yin and Yi Zhou and Chenguang Li and Shangfei Wang and Jianmin Ji and Xiaoping Chen and Ruili Wang},
  journal={2019 International Joint Conference on Neural Networks (IJCNN)},
  year={2019},
  pages={1-8}
}
  • Shi Yin, Yi Zhou, +4 authors Ruili Wang
  • Published 2019
  • Computer Science
  • 2019 International Joint Conference on Neural Networks (IJCNN)
We propose KDSL, a new word sense disambiguation (WSD) framework that utilizes knowledge to automatically generate sense-labeled data for supervised learning. First, from WordNet, we automatically construct a semantic knowledge base called DisDict, which provides refined feature words that highlight the differences among word senses, i.e., synsets. Second, we automatically generate new sense-labeled data by DisDict from unlabeled corpora. Third, these generated data, together with manually… Expand

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