# Datasets:twi_wordsim353

Task Categories: text-classification
Languages: en tw
Multilinguality: multilingual
Size Categories: n<1K
Licenses: unknown
Language Creators: expert-generated
Annotations Creators: crowdsourced
Source Datasets: original
Dataset Preview
twi1 (string)twi2 (string)similarity (float)
etwie
ɔkra
7.35
etwie
etwie
10
nwoma
krataa
7.46
kɔmputa
intanɛt
7.58
wiemhyɛn
ɛhyɛn
5.77
ketekye
ɛhyɛn
6.31
hamakasafo
nkitahodie
7.5
television
radio
6.77
kowaa
radio
7.42
panoo
television
6.19
borɔfoferɛ
borɔdweba
5.92
yaresafoɔ
yarehwɛfoɔ
7
ɔbenfoɔ
yaresafoɔ
6.62
osuani
ɔbenfoɔ
6.81
ben
osuani
4.62
ben
kwasea
5.81
adwumakuo
sikatam
7.08
sikatam
dwa
8.08
sikatam
hamakasofoɔ
1.62
sikatam
CD
1.31
sikatam
ketebɔ
0.92
sikatam
kosua
1.81
sikakorabea
sika
8.12
dua
kwaeɛ
7.73
sika
sika
9.15
ɔbenfoɔ
borɔfoferɛ
0.31
ɔbenfoɔ
ɛfan
0.23
ɔbenfoɔ
ɔhemaa
8.58
ɔbenfoɔ
akoa
5.92
ɔsɔfopanyin
ɔkyerɛkyerɛfoɔ
6.69
Yerusalem
Israel
8.46
Yerusalem
Palestinni
7.65
Maradona
bɔɔlo
8.62
bɔɔlo
bɔɔlo
9.03
Arafat
asomdwoeɛ
6.73
Arafat
ehu
7.65
Arafat
Jackson
2.5
mmara
mmaranimfoɔ
8.38
sini
nsoroma
7.38
sini
nkyeweɛ
6.19
sini
kyinnyegyefoɔ
6.73
sini
agohwɛbea
7.92
ehunumu
kafranyansa
4.88
nsa
kafranyansa
5.54
mmorɔsa
mmorɔsa
8.46
mmorɔsa
mmorɔsa
8.13
nom
ɛhyɛn
3.04
nom
aso
1.31
nom
ano
5.96
nom
di
6.87
akwadaafoforɔ
maame
7.85
nom
maame
2.65
ɛhyɛn
ɛhyɛn
8.94
adwinneɛ
sikadwinneɛ
8.96
akwantuo
akwantuo
9.29
abarimaa
aberantewa
8.83
mpoano
mpoano
9.1
abɔdamfoɔfie
abɔdamfoɔdan
8.87
sumanfoɔ
bayifoɔ
9.02
owigyinaeɛ
prɛmtoberɛ
9.29
fonoo
bukyia
8.79
aduane
aduaba
7.52
anomaa
akokɔnini
7.1
adwinnade
adwumayɛdeɛ
6.46
nuabarima
okokorani
6.27
aberantewa
nuabarima
4.46
akwantuo
ɛhyɛn
5.85
okokorani
kɔmfoɔ
5
aduane
akokɔnini
4.42
mpoano
bepɔwa
4.38
kwaeɛ
amusieeɛ
1.85
okokorani
akoa
0.92
mpoano
kwaeɛ
3.15
aberantewa
bayifoɔ
0.92
ahoma
nwenwene
0.54
ahwehwɛ
sumanfoɔ
2.08
prɛmtoberɛ
ahoma
0.54
akokɔnini
akwantuo
0.62
sika
dɔla
8.42
sika
sika
9.04
sika
ahonya
8.27
sika
agyapadeɛ
7.57
sika
agyapadeɛ
7.29
sika
sikakorabea
8.5
sika
dwumadie
3.31
etwie
ketebɔ
8
etwie
ɔkra
8
etwie
aboa
7
etwie
ateasedeɛ
4.77
etwie
aboa
5.62
etwie
mmoadoma korabea
5.87
okyinsoroma
nsoroma
8.45
okyinsoroma
ɔsrane
8.08
okyinsoroma
awia
8.02
okyinsoroma
ehunumu
7.92
kuruwa
kafe
6.58
kuruwa
nkukuo
6.85
kuruwa
adeɛ
2.4
kuruwa
adeɛ
2.92
kuruwa
adeɛ
3.69
End of preview (truncated to 100 rows)

# Dataset Card for Yorùbá Wordsim-353

### Dataset Summary

A translation of the word pair similarity dataset wordsim-353 to Twi. However, only 274 (out of 353) pairs of words were translated

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

Twi (ISO 639-1: tw)

## Dataset Structure

### Data Instances

An instance consists of a pair of words as well as their similarity. The dataset contains both the original English words (from wordsim-353) as well as their translation to Twi.

### Data Fields

• twi1: the first word of the pair; translation to Twi
• twi2: the second word of the pair; translation to Twi
• similarity: similarity rating according to the English dataset

### Data Splits

Only the test data is available

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

@inproceedings{alabi-etal-2020-massive,
title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\u}b{\'a} and {T}wi",
author = "Alabi, Jesujoba  and
Amponsah-Kaakyire, Kwabena  and
Adelani, David  and
Espa{\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
pages = "2754--2762",
abstract = "The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yor{\u}b{\'a} and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and quantify the improvements that depend not only on the amount of data but on the quality too. We also use different architectures that learn word representations both from surface forms and characters to further exploit all the available information which showed to be important for these languages. For the evaluation, we manually translate the wordsim-353 word pairs dataset from English into Yor{\u}b{\'a} and Twi. We extend the analysis to contextual word embeddings and evaluate multilingual BERT on a named entity recognition task. For this, we annotate with named entities the Global Voices corpus for Yor{\u}b{\'a}. As output of the work, we provide corpora, embeddings and the test suits for both languages.",
language = "English",
ISBN = "979-10-95546-34-4",
}


### Contributions

Thanks to @dadelani for adding this dataset.