An assessment of orthographic similarity measures for several African languages
August 10, 2016 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
C. Maria Keet
arXiv ID
1608.03065
Category
cs.CL: Computation & Language
Citations
3
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Natural Language Interfaces and tools such as spellcheckers and Web search in one's own language are known to be useful in ICT-mediated communication. Most languages in Southern Africa are under-resourced, however. Therefore, it would be very useful if both the generic and the few language-specific NLP tools could be reused or easily adapted across languages. This depends on the notion, and extent, of similarity between the languages. We assess this from the angle of orthography and corpora. Twelve versions of the Universal Declaration of Human Rights (UDHR) are examined, showing clusters of languages, and which are thus more or less amenable to cross-language adaptation of NLP tools, which do not match with Guthrie zones. To examine the generalisability of these results, we zoom in on isiZulu both quantitatively and qualitatively with four other corpora and texts in different genres. The results show that the UDHR is a typical text document orthographically. The results also provide insight into usability of typical measures such as lexical diversity and genre, and that the same statistic may mean different things in different documents. While NLTK for Python could be used for basic analyses of text, it, and similar NLP tools, will need considerable customization.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Computation & Language
๐
๐
Old Age
๐
๐
Old Age
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
๐
๐
Old Age
XLNet: Generalized Autoregressive Pretraining for Language Understanding
๐ฎ
๐ฎ
The Ethereal
Effective Approaches to Attention-based Neural Machine Translation
๐
๐
Old Age
A large annotated corpus for learning natural language inference
๐
๐
Old Age
HellaSwag: Can a Machine Really Finish Your Sentence?
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
๐ป
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
๐ป
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
๐ป
Ghosted