| 000 | 01638naa a2200229 a 4500 | ||
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| 003 | AR-LpUFIB | ||
| 005 | 20250311170453.0 | ||
| 008 | 230201s2018 xx o 000 0 eng d | ||
| 024 | 8 |
_aDIF-M7791 _b8007 _zDIF007119 |
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| 040 |
_aAR-LpUFIB _bspa _cAR-LpUFIB |
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| 100 | 1 | _aRodríguez, J. M. | |
| 245 | 1 | 0 | _aEvaluation of open information extraction methods using Reuters-21578 database |
| 300 | _a1 archivo (375,3 kB) | ||
| 500 | _aFormato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca) | ||
| 520 | _aThe following article shows the precision, the recall and the F1-measure for three knowledge extraction methods under Open Information Extraction paradigm. These methods are: ReVerb, OLLIE and ClausIE. For the calculation of these three measures, a representative sample of Reuters-21578 was used; 103 newswire texts were taken randomly from that database. A big discrepancy was observed, after analyzing the obtained results, between the expected and the observed precision for ClausIE. In order to save the observed gap in ClausIE precision, a simple improvement is proposed for the method. Although the correction improved the precision of Clausie, ReVerb turned out to be the most precise method; however ClausIE is the one with the better F1-measure. | ||
| 534 | _aInternational Conference on Machine Learning and Soft Computing (2da : 2018 : Phu Quoc, Vietnam) | ||
| 700 | 1 | _aMerlino, Hernán | |
| 700 | 1 | _aPesado, Patricia Mabel | |
| 700 | 1 | _aGarcía-Martínez, Ramón | |
| 856 | 4 | 0 | _uhttp://dx.doi.org/10.1145/3184066.3184099 |
| 942 | _cCP | ||
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_c56894 _d56894 |
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