MAG: Multilingual Entity Linking
Table 2. Micro F-measure across approaches. Red entries are the top scores while blue represents the second best scores.
['angle=90,lap=0pt-(1em) [BOLD] Language', '[BOLD] Tools/Data sets', 'angle=90,lap=0pt-(1em) [BOLD] AGDISTS', 'angle=90,lap=0pt-(1em) [BOLD] AIDA', 'angle=90,lap=0pt-(1em) [BOLD] Babelfy', 'angle=90,lap=0pt-(1em) [BOLD] DBpedia', 'angle=90,lap=0pt-(1em) [BOLD] DoSer', 'angle=90,lap=0pt-(1em) [BOLD] entityclassifier.eu', 'angle=90,lap=0pt-(1em) [BOLD] FRED', 'angle=90,lap=0pt-(1em) [BOLD] Kea', 'angle=90,lap=0pt-(1em) [BOLD] NERD-ML', 'angle=90,lap=0pt-(1em) [BOLD] PBOH', 'angle=90,lap=0pt-(1em) [BOLD] WAT', 'angle=90,lap=0pt-(1em) [BOLD] xLisa', 'angle=90,lap=0pt-(1em) [BOLD] MAG + HITS', 'angle=90,lap=0pt-(1em) [BOLD] MAG + PR']
Results on English data sets For this evaluation, we configured MAG to disambiguate named entities as well as common entities, to use the acronyms index and the popularity scores. yields an optimal choice of 2. Therefore, we tuned the thresholds to d=2 and σ=0.87 for performing the evaluation of MAG.