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Abstract
In this paper, we propose an approach to improvement of text categorization using interaction with the user. The quality of categorization has been defined in terms of a distribution of objects related to the classes and projected on the self-organizing maps. For the experiments, we use the articles and categories from the subset of Simple Wikipedia. We test three different approaches for text representation. As a baseline we use Bag-of-Words with weighting based on Term Frequency-Inverse Document Frequency that has been used for evaluation of neural representations of words and documents: Word2Vec and Paragraph Vector. In the representation, we identify subsets of features that are the most useful for differentiating classes. They have been presented to the user, and his or her selection allow increase the coherence of the articles that belong to the same category and thus are close on the SOM.
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Authors (4)
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David Gil
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Higinio Mora
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Keywords
Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- Artificial Intelligence and Soft Computing strony 265 - 275
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- ATROSZKO J., Szymański J., Gil D., Mora H.: Text Categorization Improvement via User Interaction// Artificial Intelligence and Soft Computing/ : , 2018, s.265-275
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-319-91262-2_24
- Verified by:
- Gdańsk University of Technology
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