You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
The US President Barack Obama lives in Washington, D.C.
It would be nice if propS is able to discover that US President and Barack Obama are in a SameAs relation. And D,C. should be a mod on Washington?
Actual results:
1 The _ DT DT _ 5 det _ _
2 US _ NNP NNP _ 5 nn _ _
3 President _ NNP NNP _ 5 nn _ _
4 Barack _ NNP NNP _ 5 nn _ _
5 Obama _ NNP NNP _ 6 nsubj _ _
6 lives _ VBZ VBZ _ 0 root _ _
7 in _ IN IN _ 6 prep _ _
8 Washington _ NNP NNP _ 7 pobj _ _
9 , _ , , _ 8 punct _ _
10 D.C. _ NNP NNP _ 8 appos _ _
{'lf': [{'pred': 'SameAs',
'sameAs_arg': [{'xref': ['lf', 1, 'prep_in', 0]},
{'xref': ['lf', 1, 'prep_in', 1]}],
'text': 'Washington , D.C. '},
{'pred': 'lives',
'prep_in': ['D.C. ', 'Washington '],
'subj': {'text': 'The US President Barack Obama ',
'word': ['US', 'President', 'Barack', 'Obama']},
'text': 'The US President Barack Obama lives in Washington , D.C. '}],
'sentence': 'The US President Barack Obama lives in Washington , D.C.'}
lives:(subj:The US President Barack Obama , prep_in:Washington )
lives:(subj:The US President Barack Obama , prep_in:D.C. )
SameAs:(sameAs_arg:Washington , sameAs_arg:D.C. )
Stanford NLP generates one NNP US President Barack Obama and this probably colors propS's analysis. But D.C. is identified as an appos.
Another, more complex sentence:
During May 2000 , the Serbian government intensified its efforts
to silence opposition to the government of Yugoslav President
Slobodan Milosevic.
Again, one would probably expect a SameAs between Yugoslav President and Slobodan Milosevic. Probably Stanford NLP is to blame.
Actual results:
1 During _ IN IN _ 8 prep _ _
2 May _ NNP NNP _ 1 pobj _ _
3 2000 _ CD CD _ 2 num _ _
4 , _ , , _ 8 punct _ _
5 the _ DT DT _ 7 det _ _
6 Serbian _ NNP NNP _ 7 nn _ _
7 government _ NN NN _ 8 nsubj _ _
8 intensified _ VBD VBD _ 0 root _ _
9 its _ PRP$ PRP$ _ 10 poss _ _
10 efforts _ NNS NNS _ 8 dobj _ _
11 to _ TO TO _ 12 aux _ _
12 silence _ VB VB _ 10 vmod _ _
13 opposition _ NN NN _ 12 dobj _ _
14 to _ TO TO _ 12 prep _ _
15 the _ DT DT _ 16 det _ _
16 government _ NN NN _ 14 pobj _ _
17 of _ IN IN _ 16 prep _ _
18 Yugoslav _ NNP NNP _ 21 nn _ _
19 President _ NNP NNP _ 21 nn _ _
20 Slobodan _ NNP NNP _ 21 nn _ _
21 Milosevic _ NNP NNP _ 17 pobj _ _
22 . _ . . _ 8 punct _ _
{'lf': [{'dobj': {'mod': {'dobj': 'opposition ',
'pred': 'silence',
'prep_to': {'prep_of': {'text': 'Yugoslav President Slobodan Milosevic ',
'word': ['Yugoslav',
'President',
'Slobodan',
'Milosevic']},
'text': 'the government of Yugoslav President Slobodan Milosevic ',
'word': 'government'},
'text': 'to silence opposition to the government of Yugoslav President Slobodan Milosevic '},
'poss': 'its ',
'text': 'efforts to silence opposition to the government of Yugoslav President Slobodan Milosevic ',
'word': 'efforts'},
'pred': 'intensified',
'prep_during': {'text': 'May 2000 ', 'word': ['May', '2000']},
'subj': {'text': 'the Serbian government ',
'word': ['Serbian', 'government']},
'text': 'During May 2000 , the Serbian government intensified its efforts to silence opposition to the government of Yugoslav President Slobodan Milosevic '},
{'dobj': {'xref': ['lf', 0, 'dobj']},
'pred': 'have',
'subj': {'xref': ['lf', 0, 'dobj', 'poss']},
'text': 'its have efforts to silence opposition to the government of Yugoslav President Slobodan Milosevic '}],
'sentence': 'During May 2000 , the Serbian government intensified its efforts to silence opposition to the government of Yugoslav President Slobodan Milosevic .'}
intensified:(subj:the Serbian government , dobj:its efforts to silence opposition to the government of Yugoslav President Slobodan Milosevic , prep_during:May 2000 )
have:(subj:its , dobj:efforts to silence opposition to the government of Yugoslav President Slobodan Milosevic )
The text was updated successfully, but these errors were encountered:
Consider:
It would be nice if propS is able to discover that
US President
andBarack Obama
are in aSameAs
relation. AndD,C.
should be amod
onWashington
?Actual results:
Stanford NLP generates one NNP
US President Barack Obama
and this probably colors propS's analysis. ButD.C.
is identified as anappos
.Another, more complex sentence:
Again, one would probably expect a
SameAs
betweenYugoslav President
andSlobodan Milosevic
. Probably Stanford NLP is to blame.Actual results:
The text was updated successfully, but these errors were encountered: