statistiche ricercatore

Continente	#
EU - Europa	1.645
NA - Nord America	1.196
AS - Asia	684
SA - Sud America	97
Continente sconosciuto - Info sul continente non disponibili	61
AF - Africa	39
OC - Oceania	5
Totale	3.727

Nazione	#
US - Stati Uniti d'America	1.122
RU - Federazione Russa	383
FI - Finlandia	322
IT - Italia	236
DE - Germania	222
CN - Cina	208
IE - Irlanda	179
SG - Singapore	159
VN - Vietnam	115
GB - Regno Unito	98
FR - Francia	75
BR - Brasile	70
CA - Canada	45
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	18
JP - Giappone	15
CZ - Repubblica Ceca	13
KR - Corea	13
PL - Polonia	10
CO - Colombia	8
AR - Argentina	7
BD - Bangladesh	7
RO - Romania	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
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
EE - Estonia	2
JM - Giamaica	2
KG - Kirghizistan	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
RS - Serbia	1
SA - Arabia Saudita	1
TG - Togo	1
UZ - Uzbekistan	1
ZM - Zambia	1
Totale	3.668

Nome	#
Identifying linguistic areas for geolocation	321
Geolocation with attention-based multitask learning models	309
Increasing in-class similarity by retrofitting embeddings with demographic information	301
Dense node representation for geolocation	299
Helpful or hierarchical? Predicting the communicative strategies of chat participants, and their impact on success	254
A case for soft loss functions	210
MilaNLP @ WASSA: does BERT feel sad when you cry?	203
Learning from disagreement: a survey	189
Fake opinion detection: how similar are crowdsourced datasets to real data?	186
“You sound just like your father”. Commercial machine translation systems include stylistic biases	184
ProSiT! Latent Variable Discovery with PROgressive SImilarity Thresholds	174
BERTective: language models and contextual information for deception detection	173
Gender and age bias in commercial machine translation	166
Hard and soft evaluation of NLP models with BOOtSTrap SAmpling - BooStSa	160
Beyond black & white: leveraging annotator disagreement via soft-label multi-task learning	158
“We will reduce taxes” - Identifying election pledges with language models	156
We need to consider disagreement in evaluation	151
SemEval-2021 Task 12: learning with disagreements	133
Totale	3.727

	Totale	Lug	Ago	Sett	Ott	Nov	Dic	Gen	Feb	Mar	Apr	Mag	Giu
2021/2022	163	0	0	3	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	107	48	49	10	0	0	0	0	0	0	0	0	0
Totale													3.727