Word Sense Induction (WSI) usually relies on data structures built upon the words to be disambiguated. This is a time-consuming process that requires a huge computational effort. In this paper, we propose an approach to automatically build a generic sense inventory (called iSC) to be used as a reference for disambiguation. The sense inventory is built extracting insight from Big Data exploiting a community detection algorithm. Since generate taking into account large corpora of data, the iSCis independent of the domain of application and of predefined target words.
Using Big Data to Support Automatic Word Sense Disambiguation / Guerra, Francesco; Simonini, Giovanni. - STAMPA. - (2014), pp. 311-314. (Intervento presentato al convegno Conference on High Performance Computing & Simulation tenutosi a Bologna nel 21 25 July 2014).
Using Big Data to Support Automatic Word Sense Disambiguation
GUERRA, Francesco;SIMONINI, GIOVANNI
2014
Abstract
Word Sense Induction (WSI) usually relies on data structures built upon the words to be disambiguated. This is a time-consuming process that requires a huge computational effort. In this paper, we propose an approach to automatically build a generic sense inventory (called iSC) to be used as a reference for disambiguation. The sense inventory is built extracting insight from Big Data exploiting a community detection algorithm. Since generate taking into account large corpora of data, the iSCis independent of the domain of application and of predefined target words.Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris