Automated learning of domain taxonomies from text using background knowledge.
In this paper, we present an automated method for taxonomy learning, focusing on concept formation and hierarchical relation learning. To infer such relations, we partition the extracted concepts and group them into closely-related clusters using Hierarchical Agglomerative Clustering, informed by syntactic matching and semantic relatedness functions. We introduce a novel, unsupervised method for cluster detection based on automated dendrogram pruning, which is dynamic to each partition. We evaluate our approach with two different types of textual corpora, clinical trials descriptions and MEDLINE publication abstracts. The results of several experiments indicate that our method is superior to existing dynamic pruning and the state-of-art taxonomy learning methods. It yields higher concept coverage (95.75%) and higher accuracy of learned taxonomic relations (up to 0.71 average precision and 0.96 average recall).
Department of Biomedical Informatics, Columbia University, New York, NY, USA.
Julia
J
Hoxha
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Guoqian
G
Jiang
Department of Biomedical Informatics, Columbia University, New York, NY, USA. Electronic address: chunhua@columbia.edu.
Chunhua
C
Weng
D016000:Cluster Analysis; D001330:Electronic Data Processing; D006801:Humans; D019359:Knowledge; D016239:MEDLINE; D012660:Semantics; D000069558:Unsupervised Machine Learning
Concept discovery; Ontology learning; Semantic relation acquisition; Taxonomy extraction from text; Term recognition