A combination of active learning and semi-supervised learning starting with positive and unlabeled examples for word sense disambiguation
Makoto Imamura, Yasuhiro Takayama, Nobuhiro Kaji, Masashi Toyoda, Masaru Kitsuregawa · 2009
This paper proposes to solve the bottleneck of finding training data for word sense disambiguation (WSD) in the domain of web queries, where a complete set of ambiguous word senses are unknown.In this paper, we present a combination of active learning and semi-supervised learning method to treat the case when positive examples, which have an expected word sense in web search result, are only given.The novelty of our approach is to use "pseudo negative examples" with reliable confidence score estimated by a classifier trained with positive and unlabeled examples.We show experimentally that our proposed method achieves close enough WSD accuracy to the method with the manually prepared negative examples in several Japanese Web search data.