Named entity recognition aims at locating elements in a given text and classifying them according to pre-defined categories, such as the names of persons, organisations, locations, quantities, etc. This paper proposes an approach to recognise the location names by extracting them from unstructured Italian language texts. We put forward the use of the framework MapReduce for this task, since it is more robust than a classical analysis when data are unknown and assists at parallelising processing, which is essential for a large amount of data.
Extracting Location Names from Unstructured Italian Texts Using Grammar Rules and MapReduce / Napoli, C; Tramontana, E; Verga, G. - 639:(2016), pp. 593-601. (Intervento presentato al convegno 22nd International Conference on Information and Software Technologies, ICIST 2016 tenutosi a Druskininkai; Lithuania) [10.1007/978-3-319-46254-7_48].
Extracting Location Names from Unstructured Italian Texts Using Grammar Rules and MapReduce
Napoli C
;
2016
Abstract
Named entity recognition aims at locating elements in a given text and classifying them according to pre-defined categories, such as the names of persons, organisations, locations, quantities, etc. This paper proposes an approach to recognise the location names by extracting them from unstructured Italian language texts. We put forward the use of the framework MapReduce for this task, since it is more robust than a classical analysis when data are unknown and assists at parallelising processing, which is essential for a large amount of data.File | Dimensione | Formato | |
---|---|---|---|
Napoli_Extracting-location_2016.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
201.59 kB
Formato
Adobe PDF
|
201.59 kB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.