index
int32
0
803k
hashtag
stringlengths
2
135
segmentation
stringlengths
2
164
0
BrandThunder
Brand Thunder
1
iampissed
i am pissed
2
theotherthing
the other thing
3
iran88
iran88
4
BoysPlzTakeNote
Boys Plz Take Note
5
FedPolice
Fed Police
6
ChabadLubavitch
Cha bad Lubavitch
7
BestenDank
Besten Dank
8
MeuMomentoFofoqueiro
Meu Momento Fofoqueiro
9
spiders
spiders
10
mdbs
mdbs
11
ShouldStayinBoxWhereTheyBelong
Should Stay in Box Where They Belong
12
godaan
godaan
13
ZwaarVoorSchut
Zwaar Voor Schut
14
JamesLeGros
James Le Gros
15
woodf
woodf
16
backupand
back up and
17
FeelingSvelt
Feeling Svelt
18
1stconcert
1st concert
19
iwillnevergetover
i will never get over
20
themjeans
them jeans
21
HaveYouSeenTheDragon
Have You Seen The Dragon
22
MoonFruit
Moon Fruit
23
jinglede
jingle de
24
SideEffects
Side Effects
25
carmelindiana
carmel indiana
26
UpAndToTheRight
Up And To The Right
27
blogartikel
blog artikel
28
point2
point 2
29
SweetJobasDreams
Sweet Jobas Dreams
30
TedxDublin
Tedx Dublin
31
TotallyTekeeTomatoTeam
Totally Tekee Tomato Team
32
DrakeWars
Drake Wars
33
WednesdayComicRun
Wednesday Comic Run
34
fartnoise
fart noise
35
SupportSolano
Support Solano
36
YouknoYouFromTrinidad
You kno You From Trinidad
37
stabintheback
stab in the back
38
CityFail
City Fail
39
jrpg
jrpg
40
kaushal
kaushal
41
grueling
grueling
42
foresthills
forest hills
43
sinvoz
sin voz
44
tomorrowishisbirthday
tomorrow is his birthday
45
RalphLaurenfail
Ralph Lauren fail
46
onlinespiel
onlinespiel
47
virtuosos
virtuosos
48
BestTwitNames
Best Twit Names
49
reallytrue
really true
50
InaPerfectworld
In a Perfect world
51
itwouldbesweet
it would be sweet
52
nondriving
non driving
53
BangPop
Bang Pop
54
snuggley
snuggley
55
RealGhettoShit
Real Ghetto Shit
56
socialmediaseries
social media series
57
operarock
opera rock
58
gaskets
gaskets
59
ThingsIdoAtHomeButNotInPublic
Things I do At Home But Not In Public
60
snuggles
snuggles
61
snuggler
snuggler
62
patinete
patinete
63
BiblicalCounseling
Biblical Counseling
64
howmanykids
how many kids
65
stocknews
stock news
66
ThingsYouSayInTheShower
Things You Say In The Shower
67
dullconversation
dull conversation
68
ViveNunca
Vive Nunca
69
makemeweep
make me weep
70
devergonha
de vergonha
71
EncryptStick
Encrypt Stick
72
ficandovelho
ficando velho
73
MercedesSigns
Mercedes Signs
74
TeamEli
Team Eli
75
lifeaffirming
life affirming
76
AmFortydiet
Am Fortydiet
77
gakngeh
gak ngeh
78
malting
malting
79
dayumn
dayumn
80
travelclub
travel club
81
DozenDoNots
Dozen Do Nots
82
GrowingPains
Growing Pains
83
YelpEats
Yelp Eats
84
SaintSundayShoutouts
Saint Sunday Shoutouts
85
JamieArcher
Jamie Archer
86
EnglishMajors
English Majors
87
VacationBliss
Vacation Bliss
88
SaveMobyDick
Save Moby Dick
89
EarthHour2010
Earth Hour 2010
90
RocklinToday
Rocklin Today
91
shitpresents
shit presents
92
DataRoom
Data Room
93
youcaribbean
you caribbean
94
DerSpiegel
Der Spiegel
95
WastedWednesday
Wasted Wednesday
96
CelebritySpamssonTwitter
Celebrity Spamss on Twitter
97
overacheiver
overacheiver
98
fishgodeep
fish go deep
99
wingsetc
wings etc
YAML Metadata Warning: The task_categories "structure-prediction" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other

Dataset Card for SNAP

Dataset Summary

Automatically segmented 803K SNAP Twitter Data Set hashtags with the heuristic described in the paper "Segmenting hashtags using automatically created training data".

Languages

English

Dataset Structure

Data Instances

{
    "index": 0,
    "hashtag": "BrandThunder",
    "segmentation": "Brand Thunder"
}

Data Fields

  • index: a numerical index.
  • hashtag: the original hashtag.
  • segmentation: the gold segmentation for the hashtag.

Dataset Creation

  • All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields: hashtag and segmentation or identifier and segmentation.

  • The only difference between hashtag and segmentation or between identifier and segmentation are the whitespace characters. Spell checking, expanding abbreviations or correcting characters to uppercase go into other fields.

  • There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as _ , :, ~ ).

  • If there are any annotations for named entity recognition and other token classification tasks, they are given in a spans field.

Additional Information

Citation Information

@inproceedings{celebi2016segmenting,
  title={Segmenting hashtags using automatically created training data},
  author={Celebi, Arda and {\"O}zg{\"u}r, Arzucan},
  booktitle={Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)},
  pages={2981--2985},
  year={2016}
}

Contributions

This dataset was added by @ruanchaves while developing the hashformers library.

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