This paper describes the participation of the UNIBA team in the Task 13 of SemEval-2015 about Multilingual All-Words Sense Disambiguation and Entity Linking. We propose an algorithm able to disambiguate both word senses and named entities by combining the simple Lesk approach with information coming from both a distributional semantic model and usage frequency of meanings. The results for both English and Italian show satisfactory performance.
UNIBA: Combining Distributional Semantic Models and Sense Distribution for Multilingual All-Words Sense Disambiguation and Entity Linking
BASILE, PIERPAOLO;CAPUTO, ANNALINA;SEMERARO, Giovanni
2015-01-01
Abstract
This paper describes the participation of the UNIBA team in the Task 13 of SemEval-2015 about Multilingual All-Words Sense Disambiguation and Entity Linking. We propose an algorithm able to disambiguate both word senses and named entities by combining the simple Lesk approach with information coming from both a distributional semantic model and usage frequency of meanings. The results for both English and Italian show satisfactory performance.File in questo prodotto:
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