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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. <jats:italic>RelHunter<\/jats:italic> 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 <jats:italic>RelHunter<\/jats:italic> to token classification approaches through several computational experiments on seven multilingual corpora. <jats:italic>RelHunter<\/jats:italic> 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.<\/jats:p>","DOI":"10.1007\/s13173-010-0018-y","type":"journal-article","created":{"date-parts":[[2010,8,7]],"date-time":"2010-08-07T16:40:31Z","timestamp":1281199231000},"page":"191-199","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["RelHunter: a machine learning method for\u00a0relation extraction from text"],"prefix":"10.1007","volume":"16","author":[{"given":"Eraldo R.","family":"Fernandes","sequence":"first","affiliation":[]},{"given":"Ruy L.","family":"Milidi\u00fa","sequence":"additional","affiliation":[]},{"given":"Ra\u00fal P.","family":"Renter\u00eda","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2010,8,8]]},"reference":[{"issue":"4","key":"18_CR1","first-page":"543","volume":"21","author":"E Brill","year":"1995","unstructured":"Brill\u00a0E (1995) Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging. 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