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Kunihiro Takeoka

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AAAI Conference 2020 Conference Paper

Learning with Unsure Responses

  • Kunihiro Takeoka
  • Yuyang Dong
  • Masafumi Oyamada

Many annotation systems provide to add an unsure option in the labels, because the annotators have different expertise, and they may not have enough confidence to choose a label for some assigned instances. However, all the existing approaches only learn the labels with a clear class name and ignore the unsure responses. Due to the unsure response also account for a proportion of the dataset (e. g. , about 10-30% in real datasets), existing approaches lead to high costs such as paying more money or taking more time to collect enough size of labeled data. Therefore, it is a significant issue to make use of these unsure. In this paper, we make the unsure responses contribute to training classifiers. We found a property that the instances corresponding to the unsure responses always appear close to the decision boundary of classification. We design a loss function called unsure loss based on this property. We extend the conventional methods for classification and learning from crowds with this unsure loss. Experimental results on realworld and synthetic data demonstrate the performance of our method and its superiority over baseline methods.

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AAAI Conference 2019 Conference Paper

Meimei: An Efficient Probabilistic Approach for Semantically Annotating Tables

  • Kunihiro Takeoka
  • Masafumi Oyamada
  • Shinji Nakadai
  • Takeshi Okadome

Given a large amount of table data, how can we find the tables that contain the contents we want? A naive search fails when the column names are ambiguous, such as if columns containing stock price information are named “Close” in one table and named “P” in another table. One way of dealing with this problem that has been gaining attention is the semantic annotation of table data columns by using canonical knowledge. While previous studies successfully dealt with this problem for specific types of table data such as web tables, it still remains for various other types of table data: (1) most approaches do not handle table data with numerical values, and (2) their predictive performance is not satisfactory. This paper presents a novel approach for table data annotation that combines a latent probabilistic model with multilabel classifiers. It features three advantages over previous approaches due to using highly predictive multi-label classifiers in the probabilistic computation of semantic annotation. (1) It is more versatile due to using multi-label classifiers in the probabilistic model, which enables various types of data such as numerical values to be supported. (2) It is more accurate due to the multi-label classifiers and probabilistic model working together to improve predictive performance. (3) It is more efficient due to potential functions based on multi-label classifiers reducing the computational cost for annotation. Extensive experiments demonstrated the superiority of the proposed approach over state-of-the-art approaches for semantic annotation of real data (183 human-annotated tables obtained from the UCI Machine Learning Repository).

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