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Semi-supervised Word Sense Disambiguation Using Example Similarity Graph
In this paper, to solve these problems, we propose a semi-supervised WSD method using graph embeddings based learning method in order to make effective use of labeled and unlabeled examples.
Budget Argument Mining Dataset Using Japanese Minutes from the National Diet and Local Assemblies
Budget argument mining attempts to identify argumentative components related to a budget item, and then classifies these argumentative components, given budget information and minutes.
Latent Semantic Word Sense Disambiguation Using Global Co-occurrence Information
The results of the experiments show this method is effective for word sense disambiguation in comparison with the all baseline methods.
Detection of Peculiar Word Sense by Distance Metric Learning with Labeled Examples
Then, peculiar examples are extracted using the local outlier factor, which is a density-based outlier detection method, from the updated training and test data.
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