statistiche ricercatore

Continente	#
EU - Europa	1.691
NA - Nord America	1.323
AS - Asia	686
SA - Sud America	98
Continente sconosciuto - Info sul continente non disponibili	61
AF - Africa	39
OC - Oceania	5
Totale	3.903

Nazione	#
US - Stati Uniti d'America	1.246
RU - Federazione Russa	383
FI - Finlandia	322
IT - Italia	275
DE - Germania	222
CN - Cina	208
IE - Irlanda	179
SG - Singapore	160
VN - Vietnam	115
GB - Regno Unito	102
FR - Francia	75
BR - Brasile	70
CA - Canada	46
HK - Hong Kong	40
UA - Ucraina	35
SE - Svezia	32
IN - India	28
TR - Turchia	25
ZA - Sudafrica	22
ID - Indonesia	21
IL - Israele	20
MX - Messico	19
JP - Giappone	16
CZ - Repubblica Ceca	13
KR - Corea	13
PL - Polonia	10
CO - Colombia	9
RO - Romania	8
AR - Argentina	7
BD - Bangladesh	7
MA - Marocco	6
NL - Olanda	6
BE - Belgio	5
EC - Ecuador	5
ES - Italia	5
PK - Pakistan	5
AE - Emirati Arabi Uniti	4
HU - Ungheria	4
IQ - Iraq	4
AU - Australia	3
AZ - Azerbaigian	3
EE - Estonia	3
EG - Egitto	3
IR - Iran	3
LT - Lituania	3
PA - Panama	3
PE - Perù	3
TH - Thailandia	3
VE - Venezuela	3
AT - Austria	2
DZ - Algeria	2
JM - Giamaica	2
KG - Kirghizistan	2
RS - Serbia	2
SC - Seychelles	2
SK - Slovacchia (Repubblica Slovacca)	2
BH - Bahrain	1
BO - Bolivia	1
CG - Congo	1
CH - Svizzera	1
CR - Costa Rica	1
CW - ???statistics.table.value.countryCode.CW???	1
DO - Repubblica Dominicana	1
EU - Europa	1
GR - Grecia	1
GT - Guatemala	1
HN - Honduras	1
HT - Haiti	1
JO - Giordania	1
KE - Kenya	1
KZ - Kazakistan	1
LB - Libano	1
MY - Malesia	1
NI - Nicaragua	1
NO - Norvegia	1
NZ - Nuova Zelanda	1
OM - Oman	1
PH - Filippine	1
PW - Palau	1
SA - Arabia Saudita	1
SV - El Salvador	1
TG - Togo	1
UZ - Uzbekistan	1
ZM - Zambia	1
Totale	3.844

Nome	#
Identifying linguistic areas for geolocation	328
Geolocation with attention-based multitask learning models	320
Dense node representation for geolocation	314
Increasing in-class similarity by retrofitting embeddings with demographic information	308
Helpful or hierarchical? Predicting the communicative strategies of chat participants, and their impact on success	261
A case for soft loss functions	217
MilaNLP @ WASSA: does BERT feel sad when you cry?	209
Learning from disagreement: a survey	204
Gender and age bias in commercial machine translation	196
Fake opinion detection: how similar are crowdsourced datasets to real data?	196
“You sound just like your father”. Commercial machine translation systems include stylistic biases	196
BERTective: language models and contextual information for deception detection	180
ProSiT! Latent Variable Discovery with PROgressive SImilarity Thresholds	179
Hard and soft evaluation of NLP models with BOOtSTrap SAmpling - BooStSa	171
Beyond black & white: leveraging annotator disagreement via soft-label multi-task learning	165
“We will reduce taxes” - Identifying election pledges with language models	163
We need to consider disagreement in evaluation	158
SemEval-2021 Task 12: learning with disagreements	138
Totale	3.903

	Totale	Lug	Ago	Sett	Ott	Nov	Dic	Gen	Feb	Mar	Apr	Mag	Giu
2021/2022	160	0	0	0	9	40	15	13	26	8	14	14	21
2022/2023	600	48	45	47	70	50	37	7	22	223	25	20	6
2023/2024	391	18	27	35	18	43	61	27	31	7	14	45	65
2024/2025	540	13	15	43	12	33	22	69	28	150	60	63	32
2025/2026	1.561	129	114	46	142	129	80	155	130	410	198	22	6
2026/2027	283	48	49	82	104	0	0	0	0	0	0	0	0
Totale													3.903