PubMed:32007643 / 492-712
Annnotations
LitCovid-PAS-Enju
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| EnjuParser_T86 | 0-10 | NN | denotes | Accounting |
| EnjuParser_T87 | 11-14 | IN | denotes | for |
| EnjuParser_T88 | 15-18 | DT | denotes | the |
| EnjuParser_T89 | 19-25 | NN | denotes | impact |
| EnjuParser_T90 | 26-28 | IN | denotes | of |
| EnjuParser_T91 | 29-32 | DT | denotes | the |
| EnjuParser_T92 | 33-43 | NNS | denotes | variations |
| EnjuParser_T93 | 44-46 | IN | denotes | in |
| EnjuParser_T94 | 47-54 | NN | denotes | disease |
| EnjuParser_T95 | 55-64 | NN | denotes | reporting |
| EnjuParser_T96 | 65-69 | NN | denotes | rate |
| EnjuParser_T97 | 69-70 | -COMMA- | denotes | , |
| EnjuParser_T98 | 71-73 | PRP | denotes | we |
| EnjuParser_T99 | 74-82 | VBD | denotes | modelled |
| EnjuParser_T100 | 83-86 | DT | denotes | the |
| EnjuParser_T101 | 87-95 | JJ | denotes | epidemic |
| EnjuParser_T102 | 96-101 | NN | denotes | curve |
| EnjuParser_T103 | 102-104 | IN | denotes | of |
| EnjuParser_T104 | 105-114 | JJ | denotes | 2019-nCoV |
| EnjuParser_T105 | 115-120 | NNS | denotes | cases |
| EnjuParser_T106 | 121-125 | NN | denotes | time |
| EnjuParser_T107 | 126-132 | NN | denotes | series |
| EnjuParser_T108 | 132-133 | -COMMA- | denotes | , |
| EnjuParser_T109 | 134-136 | IN | denotes | in |
| EnjuParser_T110 | 137-145 | NN | denotes | mainland |
| EnjuParser_T111 | 146-151 | NNP | denotes | China |
| EnjuParser_T112 | 152-156 | IN | denotes | from |
| EnjuParser_T113 | 157-164 | NNP | denotes | January |
| EnjuParser_T114 | 165-167 | CD | denotes | 10 |
| EnjuParser_T115 | 168-170 | TO | denotes | to |
| EnjuParser_T116 | 171-178 | NNP | denotes | January |
| EnjuParser_T117 | 179-181 | CD | denotes | 24 |
| EnjuParser_T118 | 181-182 | -COMMA- | denotes | , |
| EnjuParser_T119 | 183-187 | CD | denotes | 2020 |
| EnjuParser_T120 | 187-188 | -COMMA- | denotes | , |
| EnjuParser_T121 | 189-196 | IN | denotes | through |
| EnjuParser_T122 | 197-200 | DT | denotes | the |
| EnjuParser_T123 | 201-212 | JJ | denotes | exponential |
| EnjuParser_T124 | 213-219 | NN | denotes | growth |
| EnjuParser_R82 | EnjuParser_T86 | EnjuParser_T87 | arg1Of | Accounting,for |
| EnjuParser_R83 | EnjuParser_T89 | EnjuParser_T87 | arg2Of | impact,for |
| EnjuParser_R84 | EnjuParser_T89 | EnjuParser_T88 | arg1Of | impact,the |
| EnjuParser_R85 | EnjuParser_T89 | EnjuParser_T90 | arg1Of | impact,of |
| EnjuParser_R86 | EnjuParser_T92 | EnjuParser_T90 | arg2Of | variations,of |
| EnjuParser_R87 | EnjuParser_T92 | EnjuParser_T91 | arg1Of | variations,the |
| EnjuParser_R88 | EnjuParser_T92 | EnjuParser_T93 | arg1Of | variations,in |
| EnjuParser_R89 | EnjuParser_T96 | EnjuParser_T93 | arg2Of | rate,in |
| EnjuParser_R90 | EnjuParser_T96 | EnjuParser_T94 | arg1Of | rate,disease |
| EnjuParser_R91 | EnjuParser_T96 | EnjuParser_T95 | arg1Of | rate,reporting |
| EnjuParser_R92 | EnjuParser_T96 | EnjuParser_T97 | arg1Of | rate,"," |
| EnjuParser_R93 | EnjuParser_T98 | EnjuParser_T97 | arg2Of | we,"," |
| EnjuParser_R94 | EnjuParser_T86 | EnjuParser_T99 | arg1Of | Accounting,modelled |
| EnjuParser_R95 | EnjuParser_T102 | EnjuParser_T99 | arg2Of | curve,modelled |
| EnjuParser_R96 | EnjuParser_T102 | EnjuParser_T100 | arg1Of | curve,the |
| EnjuParser_R97 | EnjuParser_T102 | EnjuParser_T101 | arg1Of | curve,epidemic |
| EnjuParser_R98 | EnjuParser_T102 | EnjuParser_T103 | arg1Of | curve,of |
| EnjuParser_R99 | EnjuParser_T107 | EnjuParser_T103 | arg2Of | series,of |
| EnjuParser_R100 | EnjuParser_T107 | EnjuParser_T104 | arg1Of | series,2019-nCoV |
| EnjuParser_R101 | EnjuParser_T107 | EnjuParser_T105 | arg1Of | series,cases |
| EnjuParser_R102 | EnjuParser_T107 | EnjuParser_T106 | arg1Of | series,time |
| EnjuParser_R103 | EnjuParser_T99 | EnjuParser_T108 | arg1Of | modelled,"," |
| EnjuParser_R104 | EnjuParser_T99 | EnjuParser_T109 | arg1Of | modelled,in |
| EnjuParser_R105 | EnjuParser_T111 | EnjuParser_T109 | arg2Of | China,in |
| EnjuParser_R106 | EnjuParser_T111 | EnjuParser_T110 | arg1Of | China,mainland |
| EnjuParser_R107 | EnjuParser_T99 | EnjuParser_T112 | arg1Of | modelled,from |
| EnjuParser_R108 | EnjuParser_T113 | EnjuParser_T112 | arg2Of | January,from |
| EnjuParser_R109 | EnjuParser_T113 | EnjuParser_T114 | arg1Of | January,10 |
| EnjuParser_R110 | EnjuParser_T99 | EnjuParser_T115 | arg1Of | modelled,to |
| EnjuParser_R111 | EnjuParser_T118 | EnjuParser_T115 | arg2Of | ",",to |
| EnjuParser_R112 | EnjuParser_T116 | EnjuParser_T117 | arg1Of | January,24 |
| EnjuParser_R113 | EnjuParser_T116 | EnjuParser_T118 | arg1Of | January,"," |
| EnjuParser_R114 | EnjuParser_T119 | EnjuParser_T118 | arg2Of | 2020,"," |
| EnjuParser_R115 | EnjuParser_T115 | EnjuParser_T120 | arg1Of | to,"," |
| EnjuParser_R116 | EnjuParser_T121 | EnjuParser_T120 | arg2Of | through,"," |
| EnjuParser_R117 | EnjuParser_T99 | EnjuParser_T121 | arg1Of | modelled,through |
| EnjuParser_R118 | EnjuParser_T124 | EnjuParser_T121 | arg2Of | growth,through |
| EnjuParser_R119 | EnjuParser_T124 | EnjuParser_T122 | arg1Of | growth,the |
| EnjuParser_R120 | EnjuParser_T124 | EnjuParser_T123 | arg1Of | growth,exponential |
LitCovid-OGER
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T3 | 213-219 | GO:0040007 | denotes | growth |
LitCovid-OGER-BB
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T8 | 105-114 | SP_7 | denotes | 2019-nCoV |
LitCovid-sentences-v1
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| TextSentencer_T7 | 0-220 | Sentence | denotes | Accounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. |
LitCovid-TimeML
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| tok99 | 0-10 | NNP | denotes | Accounting |
| tok100 | 11-14 | IN | denotes | for |
| tok101 | 15-18 | DT | denotes | the |
| tok102 | 19-25 | NN | denotes | impact |
| tok103 | 26-28 | IN | denotes | of |
| tok104 | 29-32 | DT | denotes | the |
| tok105 | 33-43 | NNS | denotes | variations |
| tok106 | 44-46 | IN | denotes | in |
| tok107 | 47-54 | NN | denotes | disease |
| tok108 | 55-64 | VBG | denotes | reporting |
| tok109 | 65-69 | NN | denotes | rate |
| tok110 | 69-70 | , | denotes | , |
| tok111 | 71-73 | PRP | denotes | we |
| tok112 | 74-82 | JJ | denotes | modelled |
| tok113 | 83-86 | DT | denotes | the |
| tok114 | 87-95 | NN | denotes | epidemic |
| tok115 | 96-101 | NN | denotes | curve |
| tok116 | 102-104 | IN | denotes | of |
| tok117 | 105-109 | CD | denotes | 2019 |
| tok118 | 109-110 | : | denotes | - |
| tok119 | 110-114 | NN | denotes | nCoV |
| tok120 | 115-120 | NNS | denotes | cases |
| tok121 | 121-125 | NN | denotes | time |
| tok122 | 126-132 | NN | denotes | series |
| tok123 | 132-133 | , | denotes | , |
| tok124 | 134-136 | IN | denotes | in |
| tok125 | 137-145 | NN | denotes | mainland |
| tok126 | 146-151 | NNP | denotes | China |
| tok127 | 152-156 | IN | denotes | from |
| tok128 | 157-164 | NNP | denotes | January |
| tok129 | 165-167 | CD | denotes | 10 |
| tok130 | 168-170 | TO | denotes | to |
| tok131 | 171-178 | NNP | denotes | January |
| tok132 | 179-181 | CD | denotes | 24 |
| tok133 | 181-182 | , | denotes | , |
| tok134 | 183-187 | CD | denotes | 2020 |
| tok135 | 187-188 | , | denotes | , |
| tok136 | 189-196 | IN | denotes | through |
| tok137 | 197-200 | DT | denotes | the |
| tok138 | 201-212 | NN | denotes | exponential |
| tok139 | 213-219 | NN | denotes | growth |
| tok140 | 219-220 | . | denotes | . |
| lookup18 | 44-46 | country_code | denotes | in |
| lookup19 | 105-109 | year | denotes | 2019 |
| lookup20 | 134-136 | country_code | denotes | in |
| lookup21 | 146-151 | location | denotes | China |
| lookup22 | 157-164 | date | denotes | January |
| lookup23 | 168-170 | country_code | denotes | to |
| lookup24 | 171-178 | date | denotes | January |
| lookup25 | 183-187 | year | denotes | 2020 |