Fusing Multi-label Classification and Semantic Tagging

We present a three-part workflow for the combination of multi-label classification and semantic tagging using a collection of key-phrases. The workflow is illustrated on the basis of patent abstracts with the CPC classification scheme. The key-phrases are drawn from a training set collection of documents without manual interaction. The union of CPC labels and key-phrases provides a label set on which a multi-label classifier model is generated by supervised training. We show learning curves for both key-phrases and classification categories, and a semantic graph generated from cosine similarities. We conclude that, given sufficient training data, the number of label categories is highly scalable.

  • Published in:
    LWDA Lernen. Wissen. Daten. Analysen. (LWDA)
  • Type:
  • Authors:
    J. Kindermann, K. Beckh
  • Year:

Citation information

J. Kindermann, K. Beckh: Fusing Multi-label Classification and Semantic Tagging, Lernen. Wissen. Daten. Analysen. (LWDA), LWDA, 2020, https://www.researchgate.net/publication/346020921_Fusing_Multi-label_Classification_and_Semantic_Tagging, Kindermann.Beckh.2020,