P06-1056 related to the phonetic aspect of cognate identification . He used in his work algorithms
N03-2016 difference between various methods of cognate identification . Table 1 shows results of augmenting
A00-2038 new alignment algorithm with a cognate identification procedure . The alignment of
N09-3016 systems can facilitate the task of cognate identification by providing a language independent
N01-1014 compares the effectiveness of various cognate identification methods , using interpolated
N03-2016 . The results confirm that the cognate identification approach can improve the quality
N03-2016 duplication factor for three methods of cognates identification averaged over nine language pairs
N01-1014 methods to a specific task , such as cognate identification , their relative performance
N01-1014 coefficient performs poorly as a cognate identification method , being only slightly
P07-1083 and evaluate this approach on cognate identification . Section 2 describes previous
D14-1112 the results , before and after cognate identification . In the % words column we provide
C04-1137 distinct task of cross-language cognate identification . In Figure 1 , the macro-averaged
C02-1016 complex correspondences into the cognate identification algorithm by employing Melamed
N03-2016 European factor for five methods of cognates identification averaged over nine language pairs
N09-3016 translitera - tion . The existing cognate identification systems use the orthographic
C02-1016 in this paper as well as other cognate identification programs were uniformly applied
C02-1016 in the set , JAKARTA , on the cognate identification task . 3 Models of translational
C02-1016 pairs are quite challenging for a cognate identification program . In many cases , the
A00-2038 they are related . An integrated cognate identification algorithm would take as input
P07-1083 outperform traditional methods on cognate identification . Unlike many recent generative
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