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Authors: Hong Li 1 ; Sebastian Krause 1 ; Feiyu Xu 1 ; Andrea Moro 2 ; Hans Uszkoreit 1 and Roberto Navigli 2

Affiliations: 1 DFKI, Germany ; 2 Sapienza Universita di Roma, Italy

Keyword(s): Relation Extraction, Lexical Semantics, Pattern Extraction.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Natural Language Processing ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Symbolic Systems

Abstract: A new method is proposed and evaluated that improves distantly supervised learning of pattern rules for n-ary relation extraction. The new method employs knowledge from a large lexical semantic repository to guide the discovery of patterns in parsed relation mentions. It extends the induced rules to semantically relevant material outside the minimal subtree containing the shortest paths connecting the relation entities and also discards rules without any explicit semantic content. It significantly raises both recall and precision with roughly 20% f-measure boost in comparison to the baseline system which does not consider the lexical semantic information.

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Paper citation in several formats:
Li, H.; Krause, S.; Xu, F.; Moro, A.; Uszkoreit, H. and Navigli, R. (2015). Improvement of n-ary Relation Extraction by Adding Lexical Semantics to Distant-Supervision Rule Learning. In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-074-1; ISSN 2184-433X, SciTePress, pages 317-324. DOI: 10.5220/0005187303170324

@conference{icaart15,
author={Hong Li. and Sebastian Krause. and Feiyu Xu. and Andrea Moro. and Hans Uszkoreit. and Roberto Navigli.},
title={Improvement of n-ary Relation Extraction by Adding Lexical Semantics to Distant-Supervision Rule Learning},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2015},
pages={317-324},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005187303170324},
isbn={978-989-758-074-1},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Improvement of n-ary Relation Extraction by Adding Lexical Semantics to Distant-Supervision Rule Learning
SN - 978-989-758-074-1
IS - 2184-433X
AU - Li, H.
AU - Krause, S.
AU - Xu, F.
AU - Moro, A.
AU - Uszkoreit, H.
AU - Navigli, R.
PY - 2015
SP - 317
EP - 324
DO - 10.5220/0005187303170324
PB - SciTePress

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