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LitCovid-PAS-Enju

Id Subject Object Predicate Lexical cue
EnjuParser_T87 0-7 NNS denotes METHODS
EnjuParser_T88 7-8 -COLON- denotes :
EnjuParser_T89 9-11 PRP denotes We
EnjuParser_T90 12-16 VBD denotes used
EnjuParser_T91 17-21 NNS denotes data
EnjuParser_T92 22-24 IN denotes on
EnjuParser_T93 25-28 DT denotes the
EnjuParser_T94 29-35 NN denotes volume
EnjuParser_T95 36-38 IN denotes of
EnjuParser_T96 39-42 NN denotes air
EnjuParser_T97 43-49 NN denotes travel
EnjuParser_T98 50-59 VBG denotes departing
EnjuParser_T99 60-64 IN denotes from
EnjuParser_T100 65-73 NNS denotes airports
EnjuParser_T101 74-76 IN denotes in
EnjuParser_T102 77-80 DT denotes the
EnjuParser_T103 81-89 JJ denotes infected
EnjuParser_T104 90-99 NNS denotes provinces
EnjuParser_T105 100-102 IN denotes in
EnjuParser_T106 103-108 NNP denotes China
EnjuParser_T107 109-112 CC denotes and
EnjuParser_T108 113-121 VBN denotes directed
EnjuParser_T109 122-124 TO denotes to
EnjuParser_T110 125-131 NNP denotes Africa
EnjuParser_T111 132-134 TO denotes to
EnjuParser_T112 135-143 VB denotes estimate
EnjuParser_T113 144-147 DT denotes the
EnjuParser_T114 148-152 NN denotes risk
EnjuParser_T115 153-155 IN denotes of
EnjuParser_T116 156-167 NN denotes importation
EnjuParser_T117 168-171 IN denotes per
EnjuParser_T118 172-179 NN denotes country
EnjuParser_R83 EnjuParser_T87 EnjuParser_T88 arg1Of METHODS,:
EnjuParser_R84 EnjuParser_T89 EnjuParser_T90 arg1Of We,used
EnjuParser_R85 EnjuParser_T91 EnjuParser_T90 arg2Of data,used
EnjuParser_R86 EnjuParser_T91 EnjuParser_T92 arg1Of data,on
EnjuParser_R87 EnjuParser_T94 EnjuParser_T92 arg2Of volume,on
EnjuParser_R88 EnjuParser_T94 EnjuParser_T93 arg1Of volume,the
EnjuParser_R89 EnjuParser_T94 EnjuParser_T95 arg1Of volume,of
EnjuParser_R90 EnjuParser_T97 EnjuParser_T95 arg2Of travel,of
EnjuParser_R91 EnjuParser_T97 EnjuParser_T96 arg1Of travel,air
EnjuParser_R92 EnjuParser_T97 EnjuParser_T98 arg1Of travel,departing
EnjuParser_R93 EnjuParser_T98 EnjuParser_T99 arg1Of departing,from
EnjuParser_R94 EnjuParser_T100 EnjuParser_T99 arg2Of airports,from
EnjuParser_R95 EnjuParser_T100 EnjuParser_T101 arg1Of airports,in
EnjuParser_R96 EnjuParser_T104 EnjuParser_T101 arg2Of provinces,in
EnjuParser_R97 EnjuParser_T104 EnjuParser_T102 arg1Of provinces,the
EnjuParser_R98 EnjuParser_T104 EnjuParser_T103 arg1Of provinces,infected
EnjuParser_R99 EnjuParser_T104 EnjuParser_T105 arg1Of provinces,in
EnjuParser_R100 EnjuParser_T106 EnjuParser_T105 arg2Of China,in
EnjuParser_R101 EnjuParser_T91 EnjuParser_T107 arg1Of data,and
EnjuParser_R102 EnjuParser_T91 EnjuParser_T108 arg2Of data,directed
EnjuParser_R103 EnjuParser_T108 EnjuParser_T109 arg1Of directed,to
EnjuParser_R104 EnjuParser_T110 EnjuParser_T109 arg2Of Africa,to
EnjuParser_R105 EnjuParser_T112 EnjuParser_T111 arg1Of estimate,to
EnjuParser_R106 EnjuParser_T108 EnjuParser_T111 modOf directed,to
EnjuParser_R107 EnjuParser_T114 EnjuParser_T112 arg2Of risk,estimate
EnjuParser_R108 EnjuParser_T114 EnjuParser_T113 arg1Of risk,the
EnjuParser_R109 EnjuParser_T114 EnjuParser_T115 arg1Of risk,of
EnjuParser_R110 EnjuParser_T116 EnjuParser_T115 arg2Of importation,of
EnjuParser_R111 EnjuParser_T114 EnjuParser_T117 arg1Of risk,per
EnjuParser_R112 EnjuParser_T118 EnjuParser_T117 arg2Of country,per

LitCovid-ArguminSci

Id Subject Object Predicate Lexical cue
T6 0-180 DRI_Approach denotes METHODS: We used data on the volume of air travel departing from airports in the infected provinces in China and directed to Africa to estimate the risk of importation per country.

LitCovid-OGER

Id Subject Object Predicate Lexical cue
T2 39-42 PR:000022063 denotes air

LitCovid-sentences-v1

Id Subject Object Predicate Lexical cue
TextSentencer_T8 0-8 Sentence denotes METHODS:
TextSentencer_T9 9-180 Sentence denotes We used data on the volume of air travel departing from airports in the infected provinces in China and directed to Africa to estimate the risk of importation per country.

LitCovid-TimeML

Id Subject Object Predicate Lexical cue
tok97 0-7 NNP denotes METHODS
tok98 7-8 : denotes :
tok99 9-11 PRP denotes We
tok100 12-16 VBD denotes used
tok101 17-21 NNS denotes data
tok102 22-24 IN denotes on
tok103 25-28 DT denotes the
tok104 29-35 NN denotes volume
tok105 36-38 IN denotes of
tok106 39-42 NN denotes air
tok107 43-49 NN denotes travel
tok108 50-59 VBG denotes departing
tok109 60-64 IN denotes from
tok110 65-73 NNS denotes airports
tok111 74-76 IN denotes in
tok112 77-80 DT denotes the
tok113 81-89 JJ denotes infected
tok114 90-99 NNS denotes provinces
tok115 100-102 IN denotes in
tok116 103-108 NNP denotes China
tok117 109-112 CC denotes and
tok118 113-121 VBN denotes directed
tok119 122-124 TO denotes to
tok120 125-131 NNP denotes Africa
tok121 132-134 TO denotes to
tok122 135-143 VB denotes estimate
tok123 144-147 DT denotes the
tok124 148-152 NN denotes risk
tok125 153-155 IN denotes of
tok126 156-167 NN denotes importation
tok127 168-171 IN denotes per
tok128 172-179 NN denotes country
tok129 179-180 . denotes .
lookup12 9-11 stop denotes We
lookup13 74-76 country_code denotes in
lookup14 100-102 country_code denotes in
lookup15 103-108 location denotes China
lookup16 122-124 country_code denotes to
lookup17 125-131 location denotes Africa
lookup18 132-134 country_code denotes to
lookup19 168-171 person_first denotes per
event1 113-121 OCCURRENCE denotes directed
event10 135-143 OCCURRENCE denotes estimate
event18 50-59 OCCURRENCE denotes departing
event23 12-16 OCCURRENCE denotes used