| 000 | 01529naa a2200229 a 4500 | ||
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| 003 | AR-LpUFIB | ||
| 005 | 20250311170444.0 | ||
| 008 | 230201s2014 xx r 000 0 eng d | ||
| 024 | 8 |
_aDIF-M7558 _b7778 _zDIF006834 |
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| 040 |
_aAR-LpUFIB _bspa _cAR-LpUFIB |
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| 100 | 1 | _aAquino, Germán | |
| 245 | 1 | 0 | _aKeyword extracting using auto-associative neural networks |
| 300 | _a1 archivo (436,3 kB) | ||
| 500 | _aFormato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca) | ||
| 520 | _aThe large amount of textual information digitally available today gives rise to the need for effective means of indexing, searching and retrieving this information. Keywords are used to describe briefly and precisely the contents of a textual document. In this paper we present a new algorithm for keyword extraction. Its main goal is to extract keywords from text documents written in Spanish quickly and without requiring a large training set. This goal was achieved using auto-associative neural networks, also known as autoencoders, trained using only the terms designated as keywords in the training set, so that these networks can learn the features characterizing the important terms in a document. | ||
| 534 | _aCongreso Argentino de Ciencias de la Computación (20mo : 2014 : Buenos Aires, Argentina) | ||
| 650 | 4 | _aREDES NEURONALES | |
| 653 | _aminería de textos | ||
| 700 | 1 | _aHasperué, Waldo | |
| 700 | 1 | _aLanzarini, Laura Cristina | |
| 942 | _cCP | ||
| 999 |
_c56610 _d56610 |
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