statistiche ricercatore

Continente	#
EU - Europa	1.641
NA - Nord America	1.144
AS - Asia	679
SA - Sud America	92
AF - Africa	39
OC - Oceania	5
Continente sconosciuto - Info sul continente non disponibili	2
Totale	3.602

Nazione	#
US - Stati Uniti d'America	1.074
RU - Federazione Russa	383
FI - Finlandia	322
IT - Italia	235
DE - Germania	222
CN - Cina	208
IE - Irlanda	179
SG - Singapore	159
VN - Vietnam	114
GB - Regno Unito	97
FR - Francia	75
BR - Brasile	68
CA - Canada	44
HK - Hong Kong	39
UA - Ucraina	33
SE - Svezia	32
IN - India	28
TR - Turchia	25
ZA - Sudafrica	22
IL - Israele	20
ID - Indonesia	19
MX - Messico	17
JP - Giappone	15
CZ - Repubblica Ceca	13
KR - Corea	13
PL - Polonia	10
AR - Argentina	7
BD - Bangladesh	7
CO - Colombia	7
RO - Romania	7
MA - Marocco	6
NL - Olanda	6
BE - Belgio	5
ES - Italia	5
PK - Pakistan	5
HU - Ungheria	4
IQ - Iraq	4
AE - Emirati Arabi Uniti	3
AU - Australia	3
AZ - Azerbaigian	3
EC - Ecuador	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
CW - ???statistics.table.value.countryCode.CW???	1
DO - Repubblica Dominicana	1
EU - Europa	1
GR - Grecia	1
GT - Guatemala	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	3.602

Nome	#
Identifying linguistic areas for geolocation	316
Geolocation with attention-based multitask learning models	305
Increasing in-class similarity by retrofitting embeddings with demographic information	296
Dense node representation for geolocation	295
Helpful or hierarchical? Predicting the communicative strategies of chat participants, and their impact on success	251
A case for soft loss functions	208
MilaNLP @ WASSA: does BERT feel sad when you cry?	200
Fake opinion detection: how similar are crowdsourced datasets to real data?	183
Learning from disagreement: a survey	182
“You sound just like your father”. Commercial machine translation systems include stylistic biases	180
ProSiT! Latent Variable Discovery with PROgressive SImilarity Thresholds	170
BERTective: language models and contextual information for deception detection	170
Gender and age bias in commercial machine translation	160
Hard and soft evaluation of NLP models with BOOtSTrap SAmpling - BooStSa	157
Beyond black & white: leveraging annotator disagreement via soft-label multi-task learning	157
“We will reduce taxes” - Identifying election pledges with language models	153
We need to consider disagreement in evaluation	149
SemEval-2021 Task 12: learning with disagreements	129
Totale	3.661

	Totale	Lug	Ago	Sett	Ott	Nov	Dic	Gen	Feb	Mar	Apr	Mag	Giu
2021/2022	264	22	79	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	41	41	0	0	0	0	0	0	0	0	0	0	0
Totale													3.661