W99-0630 most cases , the precision of lexicon merging obtained from anti-lexicon are
W99-0630 lexicons . Finally , in addition to lexicon merging , POS mapping table is also useful
W99-0630 conservative lexeme selection during the lexicon merging process . Moreover , our model
E12-1056 coverage that can be achieved by lexicon merging . In order to validate our claim
W99-0630 Results We obtain the results on lexicon merging as shown at table 5 . The anti-lexicon
W99-0630 using anti-lexicon is lower in lexicon merging than in POS mapping rule learning
W99-0630 table 8 . The best precision for lexicon merging is obtained from T = 0.8 and
W99-0630 lexicons we can test twelve pairwise lexicon merging tasks , as shown in table 3 .
W99-0630 mapping rules at all to tackle the lexicon merging problems . Our next step will
W99-0630 experiments showing low precision on lexicon merging even using the human-generated
W99-0630 mapping rules is 8 % , but for lexicon merging it improves to about 22 % . Recall
W99-0630 lexemes in RL . I e ' r recall on lexicon merging = I 5.2 Results We obtain the
W99-0630 in each testing task using the lexicon merging algorithm described above , and
W99-0630 merged lexicons . Most of the 12 lexicon merging tasks achieve nearly more than
W99-0630 anti-threshold to smaller value . 4 Lexicon merging algorithm Given a POS mapping
W10-2505 essential and difficult problem of lexicon merging , namely how to carry over the
W99-0630 the following : ELF precision on lexicon merging eL where • EL is the set
W99-0630 define the precision and recall on lexicon merging as the following : ELF precision
W99-0630 ) } end end end Algorithm 3 : Lexicon merging algorithm by the fact that machine
W99-0630 affect the precision and recall on lexicon merging , we also ran experiments using
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