Towards Ontology-Based Training-Less Multi-label Text Classification
| Autor: | Saba Sabrin, Svenja Neitzel, Christoph Rensing, Wael Alkhatib |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Feature engineering
business.industry Computer science Feature vector 0211 other engineering and technologies Probabilistic logic 020101 civil engineering Feature selection 02 engineering and technology Ontology (information science) Machine learning computer.software_genre 0201 civil engineering Domain (software engineering) ComputingMethodologies_PATTERNRECOGNITION 021105 building & construction Classifier (linguistics) Overhead (computing) Artificial intelligence business computer |
| Zdroj: | Natural Language Processing and Information Systems ISBN: 9783319919461 NLDB |
| DOI: | 10.1007/978-3-319-91947-8_40 |
| Popis: | In the under-explored research area of multi-label text classification. Substantial amount of research in adapting and transforming traditional classifiers to directly handle multi-label datasets has taken place. The performance of traditional statistical and probabilistic classifiers suffers from the high dimensionality of feature space, training overhead and label imbalance. In this work, we propose a novel ontology-based approach for training-less multi-label text classification. We transform the classification task into a graph matching problem by developing a shallow domain ontology to be used as a training-less classifier. Thereby, we overcome the challenges of feature engineering and label imbalance of traditional methods. Our intensive experiments, using the EUR-Lex dataset, prove that our method provides a comparable performance to the state-of-the-art techniques in terms of Macro \(F_1\)-Score. |
| Databáze: | OpenAIRE |
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