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(αααα»ααααα»αααααααααΎααααΈαααααΆαααααΆαααα |
ααααΈαααααΆααααααΌαααΆαααααΆααΆααα»ααα αααααΆααααααα»αααααΆαααααααααααΆααααΌαααΆααα»ααααααα»α αα·αααααΌαααΆαααΆαααααΆαααααα ααααα·αααΎαα½ααααα·αααΆαααα½αααΆαααααΆααααΎαααααΆα α¬αααααα - ααΌα
αα
ααΉαα’αααααααΊααΆααααΆαααααααα |
αα
ααααΆα 1994 αα½αααααααΌαααΆααααα
αΌαααααααααα
αααα»αα’αΆα ααααα·αααΆαααααΌα |
ααα»ααααβααααΆααβαααβαα·αβααΆααβααΆαβαααΆαααΆαααβα’αααΈβααΆαβααΌα
ααΆαβααααΌαβα’αΆααΆαα·ααβαααα»αβααΈαααα»αβαα»ααΆα α¬βααααΆααΆαβααααααααβααΈ α‘ αα·αβααΈ α’ ααααβαααα½αβαααβαα |
αα
ααααα½αααΆααααΆαααααααΈααΆα 400 ααΆαααααα»ααα½αααααααΎαααα αααααααααααΆαα |
ααα Vance ααΆααα·ααΆαααΆ "αα½αααααΎαααΎαααΆ Trump ααΆααααΆααααΆαααααΆαααααΆααααααΌα αα·α (ααΆ) ααααΆααααΆαααααΆαααααΆαααααΆααααΉαααα»αααααααα½αααααΆαα’αΆααααααααΆαα·αααΆαααΆααααααα»ααααααΆαα·ααΆα" |
αααααΌααααααΆαααααα·ααααααααααα IRS 1001 - ααΆααααα½ααα·αα·ααααααααΆαααααααΆαααααααΆαααααΎααΆα 4 αααααΆα ααα»α |
ααα»ααααα
αααΆααααΆαα αααααΆαααΆαααΆαααΆααααααΆααα’αααΈααΆαα
αααααααΎα±ααααα
α»ααααααΆαααααα·ααααΆααα
αααααΆαα½ααααα»αααΆαααΆαααΆαααα ααΆααΆαααααΆααα
ααΆααΏαααααΆαααααα»αααΆααααααααΌαααΆαααΆαααΆααααααΆααααΉαααΆαααα α αΎαααΎααααΈααααααααΆααΆαα‘αΎααααααΎαααααααα·α
αα
ααΆααααααααΆαααΆααααααααΆααΆααΆααα·α
ααΆα |
ααΌα
αααα [ααααααα»α] ααΆαααααααααααΊαα·αα
αΆαααΆα
ααα |
αααΌαα·αβααβααβααΆαβααααβααΆβαα
βααααΆααααΆαβαααβαα·αβααΆαβααΆαβα
αΆααβαααα½αβααβ |
Medium αααααα (Set) 4x10,000 ααΈαα·α α·ααααααααα» USD 48.24 α’αα»αααααααααΆ (Regular) α α£α¦α₯α’α |
(Adam Taylor) α’αααααααΎαααΆαααααααΎααααααααΈαααα»αααΈαααα»αααΌαααα ααΆααααα
ααα·ααΈαααααααααααΆααααααααΆαααααααΆαααα·αααα·ααααααΆαααα½α αα·αα’αΆααααααΆαααααα»ααααααΆα αααα αααααΆααααΈααΆααααααΆααααααααααΎαα’ααααΆα
αααΎααααα αααααΆαα±ααααΆααααααΆαααΆαα
αΆααααααΎαααααΆαααααα½αααααααΆαα |
αααα»ααα·αααΉαααΆααΆαααΆ Feruchemist αα |
ααΆααααΊααΆα’αααααΆααααα
ααΈαααα»ααααΆαααΈααααααα’ααα
αΆααα - ααΆααααΊααΆααα
αΆαααααααα "The Apprentice" αααααΆααααΆαααααααααααααΆαααα’ - ααααα·αααααΎα±ααααΌα
ααααααααααΈαααααΆα |
ααααα waggling αααααΆααααααΆαααΊααααΆαααααα»α |
αααα»αααΆαα
αααα αα·ααα·αααααΆααααΆααΆαααααα α αΎαα
α»α
ααΈααααααααΆααααΏαααΆααααΌαα |
α’αααααααΈααΆααα·ααΆαααΆ USMS ααΆααα·αα·αααααΆααα»ααααα½αα’αααααΆαααα»ααααα½α Devyani Khobragade α αΎαααΆααααααααΆ USMS αααα»α Southern District ααααΈαααα»αααΌααααααΆαα
αΆααα
ααααΆαα
αΌα αα·αααΆααα»ααααα½ααααα Khobragade αααα’αα»αααααΆααααααΆαααααααΆα αα·ααα·ααΈααΆαααααααααΆα USMS |
ααΈαααα»ααααααααααααα
αα αα·αααΌαααα»ααααΆα αα·ααα½αααΆααα½αααα
ααα½ααααααΆαα ααααααα αα 250,000 ααΆααααΆαααααααα½α |
ααΆαααα·ααΆαααΆ "αα ααααΆα ααα ααΎα αααα" ααΆαααα·ααΆααααααααΉαααΈααΈααΆααααααα½ααααααΆααααΆαααααΆααΆα |
αααααααααΆααααΆαααααααα½ααααα
αααα»αααα αΆααααααααααααααααα»αααααααααααΆααααααΆααα’αααΆα
ααααΆα
αααΆαααααΌαααΆαααααΆαααααΆαααα
ααΉαααααΆαααααααααααααα
αααα Lacan: ααααααΆαααΌα
ααα Lardreau αααααααΆ 'ααΆααααΊααΆαααα·ααααα½ααααααΆααααα
αα
α»ααααααααα αααα½ααααααααα·αααααα»α α le chasse-canaille' (αα·ααΆααααααΆααααααααΆαααααΆααααΆαααΊα
αΆααα
ααααααΆαααααααααααΆααααΆαααααααα·α... |
αααα»αβααΆαβααΆαβααΊβα
αΆααβαααβαα
βαααααβααα’αΉααααα |
αααααααααΆαα½αααααΌαααΆαααααααΎααα
ααΎαα
α’αΈαααααΈααα·ααααααααα’αΆαααΆ 'αααα»αααΉαααΆαα’αΆαααααααα·αααα’ααΎαα·αααΌα
αααααα' |
ααα»αααα "α’ααααααααααα" ααααααααΆααΆαααααααααα»ααααα
αααααααααα»αααΎααααααααΆαα’αααααααααααα αααααΆααΌαα ααα»αααααΆααααα αΆααααααααααααααααααααα·αα’αΎααΎααΉααααα αΆαααααΆαααααα»α |
ααΆαα
αΌααααα [ααααΆαα·ααΌααΆ] αα
ααΆαααααααααααααα»ααααααΆαααΆα |
ααα Stephen Prothero, Special to CNN αααααΆααααΈα’αααααααα ααα»αααααΆαααααΆααα·ααααΆααΈαα Christopher Hitchens ααΆααααααΆαααΆααΆααααΆαααααΆαααααααΆαααααΊαα αΆααΈα ααααααΆα
αααΎαααΆααααααααα½αααΎααααΈα’αα·ααααΆααααααΆααααΆαα |
ααΆααααααΆαααααααΆααααα
αΆα ααααα αΆαα·ααααααΆαααααααααα αααααΆααααααα·ααΆαααααα·αααΆαααααΉααα»αααΆααα’αα |
αα
ααααααααααααααΆααΆαααααααα·ααΆαααΆαααααΆααααααααααα½αααααΉααα·αααΆαααααααααΆααα
αααΌααα αααα½αααΊαααααααααα
ααΉαα’αααΈ |
αααααΆααααΆααααααααααΈααααααα»α αα·αα’αα·αααααααα·α
αα½α
αααα»ααααα»ααα·ααααα αααα’ααΆαααΆα
αααΎααα
αααα»αααααααΆααΈααααΎααΈα₯ααα αα·αααΆαα’ ααΆαα½αααΉαααΆααααααΉααα·α
αα½α
αααα»α |
ααΈαααα»ααα»ααααααααα»α α’αααααΉααα½α
α αΎαααΆ HDFS ααΊααΆααααααααα―αααΆαα
ααα
αΆααααααααΌαααΆαααΆααα±ααααααΎααααΆααααΎαααααααΉααααα·ααααααΆααααααααΆα |
αααα»ααα
αα·α ααααααα½ααααααααΆααααΆααα
αααΆααααααΆαααααα»αα
ααααα’ααααααααααααΌαααααααααααα₯α‘αΌαααααααα»αααΊααααΆααααααΆααααααΆαααΈααΈαααΆααααααΆ αααααΆαααΈαααααααααα
α
αααΌααααααααααααα½αα
ααα½α 64 ααΆααα»ααααΆαααααΆαααΈααααα
αααα»αααααΆαααΆαααΎαααααα
αα
α»αααααα |
αααααΈααΆααΆαααΆααα·ααΆααααΆααΆα
αααΎαααΆααααααΉαααααααΆαα·ααααΆαααΆαααΉααα
ααααααααααΎααΆαααααα ααααΆαααΆαααααααΎαααΆααααααΆαααΆαααΆαααΉααα·ααα·α
ααααα |
αααααααααααΆαααα ααααΆαα½αα±ααααααΎααααααΆααα’ααααααααααΌαααααα½α αα·αααΉααααααΌαααΆααααααααααα αααααΊααΆααΆαααααΎααααΎααααα
αΆαααααααααααΆαα |
α¬ααααα·αααΎα’αααααΎαααααΆααααΌαααΆααααααΆααααααΉαααα·ααΆαααααΆαααα½αααΏα ααΆαα·ααααααααΆααααααΆααΆαα
αααααααααα |
Apple αα»αααΆαα·ααΆααααα’ααα αα·αααΆααααααααΆααα’αα |
αααα»ααααααα αα ααααα’αΆαααα·αααΊααΆαααααααααααΆααααΌαααΆααααααααααα
αααΆααα’αααααααααα·α 200 ααααΆαααα αΎα |
α’ααΈαααα
αΆαααααααααααΆαα WEC bantamweight Miguel Torres ααΊααΆαααΆαα·αααααΈαααα»ααα World Series of Fighting |
αααααΊαα
α
α»αααααΈα αΆααααΆα 2011 |
αα
αα
αααα·ααααΆαααααΆααα’αΆααααααα’αΆα
αα½ααα»ααααα·αααΆαα·ααΆαααααααααααααα |
ααΆααΊααΆαααΆαα·αααααααααααΎααααααα
α
α·αααααΆααΎα
αααΆααααΊααΆα’αααΈ |
[6] αααααΆααααΈααΆααα
ααααΈαα»α ααΆααααΆααααααααααα
ααΈααααααααα· Capitol Punishment: The Hard Truth About Washington Corruption From America's Most Notorious Lobbyist αααααααΌαααΆαααααα»ααααααα»ααααα·α
ααα·ααΆ ααααΆα 2011 |
ααΏααααΆαβαααβααΆαααΆαααΆαααβαααβαααααβαααααααα·βαααβααΆαβααβαα
βααΆβααΏαβααΆαααα ααα»ααααβα₯αααα·ααβααΎβααααααβααααααΆα AP ααΊβααβααΆαα |
αααααΆαααΆαααΆααααα ααααααα ααααΆααα½αααααα ααΆααααααααα·α
α»αααααααααα’ααααΆααααα αΆαααΆ ααΆαααααααααααΌαα
αααα»αααα§αααΆ α’αΆα
αααααα±ααααΆαααΆα αα·α Tories ααααΆαα±ααααααΎααΆαααααΆαα
αααα»ααααΆ ααααα½αα’αααααΆαα·αα·ααααΆαααΆααα»ααααα»αα‘ααα±ααααΆααααΆαα 56 α’αΆααα αααα»αα
αααα 59 α’αΆααα αα·ααα»αααααΆαα’αααΆα
αα
Westminster |
ααΆααααα Google |
ααΆααααααΈαααΆα
ααα»ααααα (SMs) αααααΆαα±ααααα αΌαααΆαααΎαα‘αΎααα
αααα»ααααα ααααα’αΉααα corpora cavernosum |
ααΆααααααααα½αα±ααα
αΆααα’αΆαααααα: αααααααΆαααααΆααα
α»αααΆαααααααααΆαααα
α
α»αααααααΌ (ααΆαααΎααα·αα₯αααΆαα/ααΆαααααΆαααααα·ααΈααααΎα±ααααα»ααααα
ααααΆα?) ααΆααααααααα·α
ααααΆαα
αααα·α
ααα·ααΆ 2009/2010 ααΆαααΆαααααΆααα
α»ααα
αα»ααα·ααααΌααααα
ααα»ααααααααΆα 2011 αα·αααΆααα |
α αααααααααΆ SWIV |
αααα»αα αα»αααΆααααΈαααΆααα·ααΆααα
αααα»αααα
ααααΈαααααααΆααααα½αααΆ "αααα»αα αα»α Virgin Media ααΆαααααΆααααΆααααα½αααΆαααααααααΎαα‘αΎαααα BBC Worldwide α’αααΈααΆααααααΎαααΈααΆαααΆαααααααααααΆαα·αααααααααααααΆαααΈα ααααα BBC ααΆααααααΉαααΆαααααΆαααΆαα·ααααααα Virgin Media ααααΈαααα αααααΆααααααααα·ααααααα David Tennant αα·α Richard Branson" |
ααΆαα
ααα»α
ααααΆααααα½αα
ααα½αααααααα»αα
ααααΎααααααααα αΆα ααΌα
ααΆααΏααα½ααααααααΎα±αααααα»αα
αΆααα’αΆααααααααααΆαααααα»αα’αα‘α»ααααααααΎαααααα αΆααααα Jeff Dean |
αααΆαα·ααααα»αααααΉααααΆαααα»α Fullerton ααΈααΆααααααΌαααΆααααα α
αααα·α |
α ααα»ααΌα
ααααα αΎα Empress Matilda ααααΌαααΆαααα½αααααΆααααααααααα·α αΆαααΆααΆα’ααααααααααααααααα
αααΆααααααααααα’ααααααα |
2. ααβααΉαβαααααΆααβαααααΆβα αααΆβααΈβα±ααβααα’α·αβαααβααΆααβαααα»αβα
ααα’α·α |
ααΆααα
αααααΎα 86 αα 88 ααΌαααα α’α
αααα·ααααα
αααααΆα‘ ααΌαααΆααααααΆααααααΊαααααααΆα ααΈαααααΊα Hubble ααΊααΆααΌαααΆααααααα
αααα»ααα·αααααΆααααΆα αααααα
ααααΈα
αααααααααΆα‘αΆαααααΈ |
"ααΆααΆαα’αΆααααααααΆααΆαααΆααααααααα»ααααα»αααααααα
αααααα»α αααα»αααααααα·ααα½αα±ααααΏ αααα»ααα·αα’αΆα
αααΆαααΆα ααΌααααΈααα’αΆα αΆαααααααα»αα
αΌαα
α·αααααααΎα±αααααα»αααΆαα’αΆααααααα
ααααα’α½α |
ααΆααααααΆααααααΎαα‘αΎααα
ααααααααααα·ααΈααααα
αΆαααααΆααααα RCN αααα»αααααΎαααΆααα
ααΈαααα»α Bournemouth |
ααΎααα·αααΆαα’αΆααααααΆαααααΌααα |
αααα»αβα
ααααβα’αααβααααΆααβααΆαβαααααβα’αΆαα»βαααααβα₯βααααΆαβα©α£βααΆααβ |
ααΆαααΆαααα½αααΆαααααΎα±αααα»ααααααΈααΆαααααααααα
α
αααααααααΆα 1445 αα·α 1624 ααα.α - αα»ααααααααΆααααααα’αΊααα»α - αααααααααααΈαααΆααααααααααααααΌαααΆαα
α»αααΆαααα·α
αααααααΆα 1815 αα·α 1945; ααα’αααααΆααααΈααΆααααααααααα’αΊααα»ααα
ααΎαααααα |
ααα»ααααβαααβααΆαβαα»αβααΌαβαααααβαα·αβααα»αααβα
ααβ ααααα·αααααΆαβααα»αβααααβααΆαβααΎαβαα
βααΆααβααβα’αααααααΉα |
αα
ααΈαααααΆαα·ααααααΆα α·αααααα ααα»ααααα’αααααααααα |
ααΆααααΆααααααα’ααααααααΆαα’αααααααΆααααΆ Haidor Ali α α
'Poly' α’αΆαα» 45 ααααΆαααΆα’αααααΉαααΆαααααααααααΆαααΌαααααΆααα
ααΈαααα»α Dhaka |
ααα»ααααααΆααααααΆα
αΆααααα½αα
ααα½αααΊα’αΆαααααααΆαααααα»αααΆαα
αα
αΆαααααα ααΌα
ααααα’αααααα’αΆα
αααααΆα
αααα
ααΈαααααα |
ααα»ααααβααααΌαβαα
βααΈβαααβααα α αΎαβααΆαβααααα |
ααΆααααΆαα’αΆαα» 73 ααααΆααα
ααααααΈ 10 ααα§αααΆ |
[36] Sanders ααΆααααΌααααΆαααΉαααΆαααΆααααα αΆαααααααααααααα»αααααΆαααααααΆαα½αααΉαααΆαααααΆααααααααααΆααααααα½ααα Margarethe |
αα·αααα
αα 6x10,000 |
ααΆαααααΆαααααααααααααααΌαααΆαααααΎα‘αΎααα
αααα»α TSB ααΈααααααααααα»αααΆααααα (1.2 Γ 1010 CFU mlβ1 αααααΆαααααααααααα, 5.7 Γ 109 CFU mlβ1 αααααΆαα ΞribA, 3.6 Γ 109 CFU mlβ1 αααααΆαα ΞribB) |
αααααααααα·αααααααΆααααααα»αααΆααα»ααααα
ααααααα α»αα’αΆα
ααΉαααααΌααα·ααα
αααααα
αααΆααααααΈ |
ααΆαααααααααααααΆααΆααααα CFNY α§ααααΆα αααααααααααΆααααα½αα―αααΆααΆααααΆααΈαααα·αααα»αααααΆαααΆαααααΆααα
αααΎαααΆααααααα»ααα½ααα
αααα»ααα·ααααα αααααΆααααΆααΈααααΆαααΆααα
αααΆααααααΆααααα 10 αα
αααα»αααΆααααααααα·αααα Arbitron ααααΆα 2002 ααααααααΈααα·αααα»α’αα·αααΊαα·ααααααααΆααα
αααΎαααΆααααααα»ααααααα·ααααα |
αααααααΆαααααα»ααααα! ααΎαα·αααααΆαααααααααΆααα’αα Big Action Mega Fight! ααΊααΆα αααα beat'em α‘αΎαααΎ 35 ααααΆααααΆαααααααααααΆ |
αααβααΊβααΆβααΆαβαααααβααΆαα |
αααα»ααα·αα’αΆα
αααααααααΆαααααααα’αααΈααααααα Zen ααααΎαααΆαααααα ααα»ααααααΆα
αααΎααααα·αα·αααααΎαα’α»ααααΈα αααααααα»αααΆαααααα’αααααααΎααααΆαα ααΆααααααα αΎα αα·αα
ααααΆαααααααΌα |
ααΈαααα»ααα
ααααΆα 1942 Rockefeller ααΆααααααααααΆαα·ααΆααα αΆαααα Harold Ickes ααΆαα½αααΉαααΆαααααΆαααα αααα·αααΆααααΎααααΈαααααΆαα αα»αααααααΆαααα
α±ααα’ααααααααααααααααααα»α ααααα·αααΎαααααΆαα·ααΆααα ααααααα·αααααΎαααααααΆα |
αααα»ααα
αα·α ααΆα’αΆα
αααα»αααΌαααααΌαααα ααΆαα»ααααααααΎαααΆααααααΆαααα·ααααααα½α ααααααααααΆαα
αααα»ααααααααααααα
αααα |
ααα»ααααβααΆαβααα·αααβααααβαααααααΈ Clinton α
ααααβααααα·ααΈαβααΆαααααααΆαβααΆαβαααΌααααΆαβα’ααβααΆα
αααΎαβαα |
ααΆαααΈαα½αααααΆααα»α ααΈαααα»α Seattle ααΆααααααα 3 αααα
αααα»αααΆαα’αΆαααααααααααΌαααΆααααααΆαααΆααααΆααααααααααα ααα»ααααααΆα
αααΆααααΆααααΆααΈαααα»αααΉααα·αα
αΆαααα»αααΆααΆααααΆα’αΆαααααα·αααααΆααααααα |
ααΎααααΈαααααΎααααααα 3-D ααΆαααααΆαααα Jaunt ααααΌαααΆαααααααα½αααΈααα’αΌααΈ 16 ααααα½αααααΆαα
ααΆααΌαααΆααααα½α |
ααΆααα·αα·αααα‘αΎααα·ααααααΆααααααα
α
α·αααααΊααΆα ααα»αααα½ααααα»αα
ααααα ααα»αααααααΆααα αααααΆαααααΈααααΌα
αααΆααααααΌαααΆαααΈαααα»αααΆαα
αααΎαααΎααααΈα’αα»αααααΆαα’αα»ααααΆα |
13. βα’ααα/αα½ααα/αααααΆαααααααΆαααααααΆαααα½ααααα |
" ααΌα
αααα’αααα’αΆαααααααΆααΉα ααΎααααΈαααα’αααΈααααααααααΆαααααααααααααΆ ααΆααααααΆααααααΆααααααααααα½αα α
ααΆ αααααααααα’ααααααααααααααααα·αα ααααΌαααΆααααααΎαα‘αΎαααα Political Voter Index α¬ P20VI |
ααα»ααααααΆαααΆαααααααααααααααΆααααααααΆαα
ααααααΆαααΆαααΈα
α»ααααα»α (L. M. Barr αα·α H. P. Possingham Mar. Policy 42, 39β48; 2013) ααΆααααα αΆαααΈαα»ααα·ααααα·αα
αααα»ααααααΆααααααΆαααααΎα‘αΎα ααααα½αααΆααα»ααααα»ααααααΆαααααΆαααΆααα’ααααααΌαααΆαα αΆαααΆαα ααααΌα
ααΆααααααααααααΆα αα·ααααααααΆαααααααααααααΌαααΆαα’αα»ααααΆα |
αααα·αααα»αα½αααααααααΆαααα»ααααα’ |
ααΆαααΆααααααΆααααααααα’αα·ααΆααααα»αααα ααΆαααααα½ααααααααααααααΈαα·αααααααα·ααααααααΆααα αα·αααΌααααΈααααΆααααααααααααΆαα·ααα ααααα½αααΆααΆααα’αααααααααΌαααΆααααααΉααααααα’αααΈααα·ααΆααΆααααααΆααα
αααα»αααααααααααααααααΌαααα’αΆααααα·αααααΆααΆαα·αααΈααααααα Donald Trump |
ααα»αααααα
ααααααααΆαααααΆααααααΌαααΆααααα
αα αααααΆααααααΆααααααααααΌαααΆααααααΎαα‘αΎααα½α
α αΎα |
Wally αα·α Tessie ααΉαααααΎααΆααα
αΆααααααααααΉαααα·ααΆαααααα α αΎαααΉαααααΌαααΆαα’αααα Curious Creatures αα·α
α·ααααααααααααα α’αααααΌααα»α αα·α Big Joe the Storyteller (12:30-1:30 p.m.) |
αα·αααΆααααααααααααΈααΆαα½ααααα»αα
αααααααααααααααΈααΆααααααΆααα½αααΌαααααααααα±ααααΆαααααΈαααααααΆαααΈααααΆααα»αααααΌαααΆαααΆαααααααααα |
ααΆαα·αααΌα
ααΆ "α’αΌ ααΎαααΉαααα Dodgers ααΎαααΉαααΆααααΌαααααααΎα" α₯α‘αΌααααααΎαααΉαααΆααΎαα’αΆα
ααααααααΎα’αααααΆααααΆααααΆα" |
ααΆααααααααΌα BC α
α»αααααααααα»αααααΌαααΆαα’αα»αααααα
ααααΆα 1990 |
αα»ααα»αα
α·αααα±ααααα αααααΈα‘αΆαααααΎαα smartkey ααα
αααα½α
ααΆαααα α±αααααααα αα |
ααααααααα Cutler ααΉααααα
αααααααΆαααααΈααααααααΆαα
αααΎαααΈα’αααααΆαααα ααααα·ααααα·ααΆαααΆααααΌαααΊααΆααααααααααα
αΆααααααΆα |
Grenzo ααΆαααααα
α’αΈααααααΆααααΆαα½ααα»ααααα·α |
- α’αΆααΌα "ααΆααα·ααΈαα»αα
αααΎααααα»αααΆααααααααΆα ααα»ααααααααΆαααααΌαααααΌααα ααΆααααααααΆαα·α
αα
|
αααααααααα·α Pritchard α’αααααααΆαααααΆαα·α’αΆαααα·αααΆααα·ααΆαααΆ "αα
αααα»αααααααααααααΆαα’αα·αααα ααΆααΆαααααΆαααΆαα·ααααα·αα½α" |
αααα»αααααααααΆααααΆ Neville Chamberlain αα·α Winston Churchill ααααΌαααΆαααα α
αα½α
α αΎα αααααΆαααααΆα
αααΆααααΆαααααααΆααααααααΆ Munich αααα»ααααα»αα‘αΎααα
αααα»α Whitehall αα·αααΆαα·ααααααΆαααΆα αΆααα
Downing Street |
α¬ααααααααΎαα
αα
α»ααααααααΆαααΆαα½αααΉααααααααΆααα
ααΎαααααααααααΎα α¬αα
ααΎ Facebook! |
αααααααααααααΌαααα½αααΆαααΆααα
ααΆαααααααα·ααΌαα Emske α’ααααα
ααΆα‘αΌα ααα JobMob ααΆαααααΌαααααααα·ααΌαααα’ααα
αΆααα |
" ααΆαααΆαααΆααα
ααααΆα α αΎαααΆαααΎαααΆ ααΆαα
ααααΈααααααααααΆ αααα½ααα
ααΎααααΆαααα (αααααααΆα) αααααα
ααααΈαααα α αΎα "αααα»αααΆαααΎα ααααΆααααααΌα ααΌα
ααΆαααα»ααααααααα½ααααα ααααααΆααααΈααααΆαααααααΆαααΆααααΌααααααΆααα’αΆα αΆααααααααΆα
" |
ααΆααΆααα·αααΆαα ααΎααααααααΏα Gossip Girl α ααααΉαα
ααααααΎααα
α
α»ααααααΆα ααα
α―ααΉαααΈ Bernie |
ααααΈααΆαααααααααααα
ααααΈααΆαααααα½α - αααααΆααααΆαα Brumbies αα·α Wallabies - ααΉαααααΆαααΆααα½αααΎααααααΆαααααΎα±αα David Pocock ααααΆααααΎαα
ααααΈααΈααΆααα
ααΆααΈααΈ 13 |
ααΆααααΆαααααΎααΆ Asiatics ααΆααα’αααα½αααααΆαααααααααα·ααα |
αααααΆααααααΆ ααΎαααααΌαααα’ααααΆαααααΆααα
ααα½α $45,000 αααααΆααααΆαα
αααΆααα
αα
αααααααααΆαα’αα»αααααααααααα Bike Share |
Khmer + English Mixed Text Corpus
A cleaned, deduplicated Khmer text corpus with naturally-occurring Khmer/English
code-switching (English tech & finance terms, Latin script, and digits embedded in Khmer
text). It is the training data for the
Panhapich/khmer-sp-8k SentencePiece tokenizer and the
text-only warm-start of a shared Khmer diffusion decoder.
Dataset summary
- 4,893,739 sentences, one per line, UTF-8, deduplicated and shuffled (fixed seed 42, reproducible).
- Two parallel versions of the same corpus are provided as separate configs β see Dataset structure.
- Khmer/English mixing is entirely organic: it comes from the real sources below (receipt line items, tech/finance terms in headlines and raw text). No synthetic code-switching is injected anywhere in this corpus.
Dataset structure
Configs / files
| Config | File | Description |
|---|---|---|
raw (default) |
all_text.txt |
Natural Khmer text, no artificial word-boundary spaces. General-purpose use. |
segmented |
all_text_segmented.txt |
Same 4,893,739 sentences, run through khmer-nltk word segmentation + gazetteer/Latin masking (see Panhapich/khmer-sp-8k). Ready for SentencePiece-style subword training without re-running segmentation yourself. |
from datasets import load_dataset
raw = load_dataset("Panhapich/khmer-text-corpus", "raw") # or omit the config name, it's the default
segmented = load_dataset("Panhapich/khmer-text-corpus", "segmented")
Data instances
Each row is a single field, text: one normalized sentence (or, in the segmented config,
one word-segmented sentence with explicit spaces between Khmer words).
Data splits
A single train split β this is raw pretraining/tokenizer-training text, not a supervised
task with held-out evaluation labels.
Dataset creation
Source data
Built from exactly four sources, mixed together, deduplicated, and shuffled. A global cap
(5,000,000 sentences) bounds the total β small sources and the local file are
taken in full, and the large raw-text source fills the remainder:
| Source | Field | Description | Collected (pre-dedup) | Share |
|---|---|---|---|---|
nphearum/khmer-raw-text-3M-v2 |
text | Large raw Khmer text (split on the Khmer full stop α) |
4,836,355 | 96.7% |
Sokheng/khmer-synthetic-ocr-v1-100k |
text | Synthetic OCR receipt/label text (Khmer/English/digit mixed) | 93,293 | 1.9% |
khmer_corpus.txt |
line | Pre-chunked ~1000-character Khmer text blocks (local file) | 35,373 | 0.7% |
rinabuoy/aupp-assignment-data |
title | Khmer news headlines | 34,991 | 0.7% |
The Khmer/English mixing is entirely organic β it comes from the real sources above. No synthetic code-switching is injected.
Curation / preprocessing
Every sentence is normalized: coerce to string, strip, replace newlines/tabs with a single
space, collapse repeated whitespace, and drop empty/invalid labels (???, NULL, N/A,
UNKNOWN). Khmer word-spacing, punctuation, Khmer + Arabic numerals, and English/Latin tokens
are preserved. Hub-source sentences are kept only if 5-400 characters; the local blocks are
exempt from the length cap. The corpus is deduplicated and shuffled with a fixed seed (42) for
reproducibility.
The segmented config additionally runs khmer-nltk word segmentation, with English/digit
spans and known loanwords/acronyms (ATM, ABA, PDF, ...) masked out beforehand so the segmenter
doesn't shatter them, and glued English/Latin runs (e.g. tryAImodel) further decomposed via
frequency-based word segmentation, guarded by a domain exception list (ACLEDA, Bakong, COVID-19,
5G, ...) so real terms aren't mangled. Full pipeline: khmer_segmentation.py in
Panhapich/khmer-sp-8k.
Considerations for using the data
- Known noise: the local
khmer_corpus.txtsource and the raw-web-scraped Hub source contain some non-standard spelling, stray symbols, and OCR artifacts. This is real-world scraped text, not a curated, edited corpus. - Not filtered for PII beyond the basic invalid-label rules above.
- Plain text only β no labels, translations, or task annotations.
- Segmentation is a statistical CRF model, not a hand-built rule system β it generalizes
well to ordinary Khmer but was not evaluated line-by-line against this specific corpus; treat
the
segmentedconfig as "reduces cross-word token fusion," not "hand-verified perfect."
Licensing
This corpus is derived from third-party datasets and inherits their licenses. Confirm the terms
of each source above and set the license field accordingly β other is a placeholder.
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