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:
Inproceedings - Authors:
J. Kindermann, K. Beckh - Year:
2020
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,
@Inproceedings{Kindermann.Beckh.2020,
author={J. Kindermann, K. Beckh},
title={Fusing Multi-label Classification and Semantic Tagging},
booktitle={Lernen. Wissen. Daten. Analysen. (LWDA)},
journal={LWDA},
url={https://www.researchgate.net/publication/346020921_Fusing_Multi-label_Classification_and_Semantic_Tagging},
year={2020},
abstract={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...}}