W12-3153 domain adaptation are selected via cross-lingual retrieval . In a probabilistic retrieval
C02-1008 on bilingual dictionaries for cross-lingual retrieval . In these systems , queries
P03-1048 . We report results using the cross-lingual retrievals in a separate paper . measures
W12-3153 table 5 ) . 6 Error Analysis Our cross-lingual retrieval approach succeeded in finding
N04-1036 NE translation pairs using the cross-lingual retrieval approach . Given a Chinese NE
W00-1312 1999 . Our work has focused on cross-lingual retrieval . Many approaches to cross-lingual
P14-2080 associations from relevance rankings to cross-lingual retrieval in the patent domain . Both show
W09-1602 utterances . In addition to a cross-lingual retrieval system built using only the known
W00-1312 relative performance ratio of cross-lingual retrieval to mono-lingual . Relative performance
E12-1012 language of the collection . The cross-lingual retrieval is performed as follows : •
W12-3153 retrieval results , we conducted cross-lingual retrieval in both directions : retrieving
P14-2080 rankings is useful in monolingual and cross-lingual retrieval . Sokolov et al. ( 2013 ) apply
S07-1001 developed by Irion technologies as a cross-lingual retrieval system ( Vossen et al. , ) .
W15-4922 translations and hypergraphs for the cross-lingual retrieval of target matches as well as
W11-4502 guages . Related to this is work on cross-lingual retrieval : -LSB- Hakkani-T ¨ ur et
N04-1036 estimated in the similar way . 4 Cross-lingual Retrieval for NE Transla - tions Two similarity
W12-3153 domain adaptation techniques . 3 Cross-Lingual Retrieval via Statistical Translation 3.1
W03-1508 method of data selection . 4.2 Cross-Lingual Retrieval Performance -- II We reran the
P06-2031 semantics is potentially useful for cross-lingual retrieval , machineaided and machine translation
W13-5001 classification ( Suzuki , 2005 ) , cross-lingual retrieval ( Noh et al. , 2009 ) , and recognizing
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