Automatic Extraction of Hierarchical Relations from Text [chapter]

Ting Wang, Yaoyong Li, Kalina Bontcheva, Hamish Cunningham, Ji Wang
2006 Lecture Notes in Computer Science  
Automatic extraction of semantic relationships between entity instances in an ontology is useful for attaching richer semantic metadata to documents. In this paper we propose an SVM based approach to hierarchical relation extraction, using features derived automatically from a number of GATE-based open-source language processing tools. In comparison to the previous works, we use several new features including part of speech tag, entity subtype, entity class, entity role, semantic representation
more » ... of sentence and WordNet synonym set. The impact of the features on the performance is investigated, as is the impact of the relation classification hierarchy. The results show there is a trade-off among these factors for relation extraction and the features containing more information such as semantic ones can improve the performance of the ontological relation extraction task. Using SVM for Relation Extraction SVM is one of the most successful ML methods, which has achieved the state-ofthe-art performances for many classification problems. For example, our experiments in [13] showed that the SVM obtained top results on several IE benchmarking corpora.
doi:10.1007/11762256_18 fatcat:fknmei4gezdznev7fdxibrei6i