TY - JOUR

T1 - RelHunter

T2 - A machine learning method for relation extraction from text

AU - Fernandes, Eraldo R.

AU - Milidiú, Ruy L.

AU - Rentería, Raúl P.

N1 - This work was partially funded by CNPq and FAPERJ grants 557.128/2009-9 and E-26/170028/2008. The first author holds a CNPq doctoral fellowship and is supported by Instituto Federal de Educação, Ciência e Tecnologia de Goiás, Brazil.

PY - 2010/9

Y1 - 2010/9

N2 - We propose RelHunter, a machine learning-based method for the extraction of structured information from text. RelHunter's key idea is to model the target structures as a relation over entities. Hence, the modeling effort is reduced to the identification of entities and the generation of a candidate relation, which are simpler problems than the original one. RelHunter fits a very broad spectrum of complex computational linguistic problems. We apply it to five tasks: phrase chunking, clause identification, hedge detection, quotation extraction, and dependency parsing. We compare RelHunter to token classification approaches through several computational experiments on seven multilingual corpora. RelHunter outperforms the token classification approaches by 2.14% on average. Moreover, we compare the derived systems against state-of-the-art systems for each corpus. Our systems achieve state-of-the-art performances for three corpora: Portuguese phrase chunking, Portuguese clause identification, and English quotation extraction. Additionally, the derived systems show good quality performance for the other four corpora.

AB - We propose RelHunter, a machine learning-based method for the extraction of structured information from text. RelHunter's key idea is to model the target structures as a relation over entities. Hence, the modeling effort is reduced to the identification of entities and the generation of a candidate relation, which are simpler problems than the original one. RelHunter fits a very broad spectrum of complex computational linguistic problems. We apply it to five tasks: phrase chunking, clause identification, hedge detection, quotation extraction, and dependency parsing. We compare RelHunter to token classification approaches through several computational experiments on seven multilingual corpora. RelHunter outperforms the token classification approaches by 2.14% on average. Moreover, we compare the derived systems against state-of-the-art systems for each corpus. Our systems achieve state-of-the-art performances for three corpora: Portuguese phrase chunking, Portuguese clause identification, and English quotation extraction. Additionally, the derived systems show good quality performance for the other four corpora.

KW - Entity relation extraction

KW - Entropy Guided Transformation Learning

KW - Machine learning

KW - Natural language processing

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=84870389863&partnerID=8YFLogxK

UR - https://link.springer.com/journal/13173/volumes-and-issues/16-3

U2 - 10.1007/s13173-010-0018-y

DO - 10.1007/s13173-010-0018-y

M3 - Journal articles

AN - SCOPUS:84870389863

VL - 16

SP - 191

EP - 199

JO - Journal of the Brazilian Computer Society

JF - Journal of the Brazilian Computer Society

SN - 0104-6500

IS - 3

M1 - 18

ER -