by transcript | by glosses | by right neighbours | by left neighbours
1584855 lei13 | 46-60m
The border installations, the Wall itself and the customs control were quite massive.
r I2 TO-DRIVE-A-CAR1* VERY7* BIG7^ CUSTOMS3* I2 TO-SEE1*
l
m [MG] zoll
1184536 nue03 | 31-45m
The idea of German as the standard language in the U.S. is crazy.
r IF-OR-WHEN1A REALLY2* IF-OR-WHEN1A BIG7^ GERMAN1 AMERICA1* BIG7^
l
m wenn deutsch amerika
1184536 nue03 | 31-45m
The idea of German as the standard language in the U.S. is crazy.
r BIG7^ GERMAN1 AMERICA1* BIG7^ GERMAN1 TO-SIGN1A* VERY6*
l
m deutsch amerika deutsch [MG]
1209077 mue06 | 18-30f
I think that your vocabulary gets extended so you know more words.
r TOGETHER2B^ WORD1 $INDEX1 BIG7^ YES1A TO-INCLUDE-OR-TO-ADMIT1*
l
m … [MG] ja [MG]
1433543 mue07 | 18-30m
It’s huge.
r BIG7^*
l
m [MG]
1289623 mst01 | 46-60f
Everyone did it that way.
r BIG7^*
l $INDEX1 $INDEX1 $INDEX1
m auch auch auch
1583964 lei09 | 31-45f
It's not a big restaurant.
r RESTAURANT1 BIG7^* AREA1D^* I1*
l $INDEX1* LOCATION1A^*
m restaurant [MG]
1289793 mst02 | 18-30f
Opposite there is a huge park.
r $INDEX1* OPPOSITE-OF1* LOCATION1A* BIG7^* AREA1A^* $ALPHA1:P-A-R-K PARK1A
l LOCATION1A
m da park typisch park park
1184536 nue03 | 46-60m
English was in the lead, and as a result became the standard language of the whole country.
r BARELY1 ENGLAND2* DONE2* BIG7^* $GEST^
l
m knapp englisch
1246100 fra11 | 18-30m
It’s a huge country.
r MEANING1* BIG7^* $GEST^
l
m bedeutet [MG]
1181159 stu09 | 18-30m
There are more smaller groups that keep to themselves now. Back in the days, we all got together, even the hard of hearing that enjoyed sign language were part of a larger group. That was awesome.
r BIG7^* ALSO1A* GROUP1C^* PAST-OR-BACK-THEN1*
l
m auch gesellschaft gesellschaft früher
1433543 mue07 | 31-45m
There are a lot more lakes in the south than in the north.
r LITTLE-BIT6 LAKE5* BIG7^* MUCH1A* LOCATION1A* AREA1D^*
l SOUTH1B
m wenig see süd see see see viel
Mouth: groß
Translational equivalent: big, large
by transcript | by glosses | by right neighbours | by left neighbours
1249741 mvp01 | 18-30m
For example there is a quite big, old castle.
r GOOD1^ FORTRESS2^ $GEST-OFF1^ BIG7
l
m schloss
1292458 mst14 | 18-30m
Especially when you compare it to France, we have a lot of big cities here.
r PARTICULARLY1B BIG7 CITY2* MUCH1C* PRESENT-OR-HERE1*
l
m besonders großstadt viel da
1292458 mst14 | 18-30m
They have many different cities with different sizes and numbers of inhabitants.
r WITH1A $GEST-TO-PONDER1^* DISTINCT1* BIG7 SMALL6*
l
m mit verschieden [MG]
1181159 stu09 | 31-45m
When I was there, there were plenty of nuns around.
r I2 NUN1* I2* BIG7
l
m … schwester [MG]
1433543 mue07 | 18-30m
It has an area of 17,000 square meters. That’s enormous.
r SQUARE1* $NUM-TEEN2A:7d $NUM-THOUSANDS1* BIG7 ROUND12^
l
m quadratmeter siebzehntausend [MG] [MG]
1292458 mst14 | 18-30m
It’s huge.
r BIG7 A-WHOLE-LOT2*
l
m [MG] groß
1585453 lei15 | 18-30f
Some are small, some are big.
r SOMETIMES1 TETRAGON10^* SOMETIMES1 BIG7 $GEST-OFF1^
l
m manchmal klein groß
1177860 sh05 | 61+m
When I think of your company’s premises, I think it would be a good idea to install a flea market once they are all empty.
r YOUR1 COMPANY1A* BIG7 HERE1 I1 TO-SEE1*
l
m dein firma [MG] [MG]
1184749 nue04 | 31-45m
Well, I walked around and there was a shopping mall somewhere which was really huge.
r LIKE3A* TO-SHOP1* CENTRE1A* BIG7 LIKE1A*
l
m wie einkaufszentrum [MG] wie
1249376 goe10 | 46-60m
How does, for example, a big company, a big/
r EXAMPLE2 BIG7* COMPANY1B BIG7 $GEST-OFF1^
l
m beispiel groß firma [MG]
1249376 goe10 | 46-60m
The big companies would have a problem, because they need more electricity.
r $GEST-OFF1^* PROBLEM1* COMPANY1B BIG7 $GEST-OFF1^* WHAT-DOES-IT-LOOK-LIKE1 $GEST-OFF1^*
l
m problem firma groß [MG] wie
1584198 lei10 | 31-45m
Well, Lufthansa is quite a big company, though.
r AIR1* AIRPLANE1^ BIG7 COMPANY1A $INDEX2 I1*
l $INDEX1
m lufthansa groß firma
1177054 hh02 | 31-45f
It is just a sign. The sign can’t be as big just because the canal is broad.
r FOR1* CLEAR1A^ SIGN3A* BIG7* WEIRD-STRANGE2* ROUND12^*
l
m für mich schild [MG] [MG] [MG]
1209910 nue09 | 18-30m
It was even bigger in Hamburg.
r $INDEX1 HAMBURG1* $INDEX1 BIG7* $INDEX1
l
m ham{burg} groß
1413251 stu07 | 46-60m
One could do something similar to what we’re doing right here, on a bigger scale, and create a corpus.
r $INDEX1* BIG7* SAME2A* $INDEX1* ALSO3A
l
m groß selbe [MG] auch
1248400 goe05 | 46-60m
It's not feasible in Germany because it's so big.
r CAN1* GERMAN1* WHAT1A BIG7* $GEST-OFF1^
l
m deutsch was zu groß
1248400 goe05 | 46-60m
If you compare Switzerland and Germany in size, Switzerland is small.
r $INDEX1* GERMAN1 BIG7* $INDEX1* ROUND12^* $GEST-OFF1^
l LOCATION3^
m [MG] [MG] deutsch groß klein
1289623 mst01 | 46-60f
There was a huge buffet.
r BIG7* TO-EAT-OR-FOOD2* BUFFET1* BUFFET1*
l
m gro{ß} büfett [MG]
1209006 mue02 | 18-30m
It was a really big room and there were a lot of international guests there.
r BIG7* $INDEX1 INTERNATIONAL1 MASS-OF-PEOPLE-ACTIVE1
l
m [MG] international [MG]
1249376 goe10 | 46-60m
How does, for example, a big company, a big/
r PROCEEDING1A EXAMPLE2 BIG7* COMPANY1B BIG7 $GEST-OFF1^
l
m ablauf beispiel groß firma [MG]
1249376 goe10 | 46-60m
That’s a very big company.
r COMPANY1A BIG7* $GEST-OFF1^*
l
m firma [MG]
1433543 mue07 | 31-45m
The third one is in the English garden. These are the biggest ones.
r $LIST1:3of3d $LIST1:4of4d BIG7* $NUM-ONE-TO-TEN1A:3d BIG7* $NUM-ONE-TO-TEN1A:3d
l $INDEX2
m oder groß groß drei
1433543 mue07 | 31-45m
The third one is in the English garden. These are the biggest ones.
r BIG7* $NUM-ONE-TO-TEN1A:3d BIG7* $NUM-ONE-TO-TEN1A:3d
l $INDEX2
m oder groß groß drei
1433543 mue07 | 31-45m
The biggest beer garden is the one in the English garden, I think.
r BIG7*
l MOST1A TO-BELIEVE2A* I1
m groß glau{ben}
1582205 lei01 | 18-30m
There are way to many stores in Dresden, but few people living there. That’s no good.
r IN1 DRESDEN1* MUCH1C* BIG7* DEAL1* APARTMENT1A* LITTLE-BIT9
l
m in dresden viel geschäft wohnen wenig