5 research outputs found
Text Categorization Can Enhance Domain-Agnostic Stopword Extraction
This paper investigates the role of text categorization in streamlining
stopword extraction in natural language processing (NLP), specifically focusing
on nine African languages alongside French. By leveraging the MasakhaNEWS,
African Stopwords Project, and MasakhaPOS datasets, our findings emphasize that
text categorization effectively identifies domain-agnostic stopwords with over
80% detection success rate for most examined languages. Nevertheless,
linguistic variances result in lower detection rates for certain languages.
Interestingly, we find that while over 40% of stopwords are common across news
categories, less than 15% are unique to a single category. Uncommon stopwords
add depth to text but their classification as stopwords depends on context.
Therefore combining statistical and linguistic approaches creates comprehensive
stopword lists, highlighting the value of our hybrid method. This research
enhances NLP for African languages and underscores the importance of text
categorization in stopword extraction.Comment: A Project Report for the Masakhane Research Communit
MasakhaNEWS: News Topic Classification for African languages
African languages are severely under-represented in NLP research due to lack
of datasets covering several NLP tasks. While there are individual language
specific datasets that are being expanded to different tasks, only a handful of
NLP tasks (e.g. named entity recognition and machine translation) have
standardized benchmark datasets covering several geographical and
typologically-diverse African languages. In this paper, we develop MasakhaNEWS
-- a new benchmark dataset for news topic classification covering 16 languages
widely spoken in Africa. We provide an evaluation of baseline models by
training classical machine learning models and fine-tuning several language
models. Furthermore, we explore several alternatives to full fine-tuning of
language models that are better suited for zero-shot and few-shot learning such
as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern
exploiting training (PET), prompting language models (like ChatGPT), and
prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API).
Our evaluation in zero-shot setting shows the potential of prompting ChatGPT
for news topic classification in low-resource African languages, achieving an
average performance of 70 F1 points without leveraging additional supervision
like MAD-X. In few-shot setting, we show that with as little as 10 examples per
label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of
full supervised training (92.6 F1 points) leveraging the PET approach.Comment: Accepted to IJCNLP-AACL 2023 (main conference
MasakhaNEWS:News Topic Classification for African languages
African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. While there are individual language specific datasets that are being expanded to different tasks, only a handful of NLP tasks (e.g. named entity recognition and machine translation) have standardized benchmark datasets covering several geographical and typologically-diverse African languages. In this paper, we develop MasakhaNEWS -- a new benchmark dataset for news topic classification covering 16 languages widely spoken in Africa. We provide an evaluation of baseline models by training classical machine learning models and fine-tuning several language models. Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API). Our evaluation in zero-shot setting shows the potential of prompting ChatGPT for news topic classification in low-resource African languages, achieving an average performance of 70 F1 points without leveraging additional supervision like MAD-X. In few-shot setting, we show that with as little as 10 examples per label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of full supervised training (92.6 F1 points) leveraging the PET approach
Anhaltender idiopathischer Gesichtsschmerz und atypische Odontalgie
The terms ‘persistent idiopathic facial pain’ (PIFP) and ‘atypical odontalgia’ (AO) are currently used as exclusion diagnoses for chronic toothache and chronic facial pain. Knowledge about these pain conditions in medical and dental practices is of crucial importance for the prevention of iatrogenic tissue damage by not-indicated invasive interventions, such as endodontic treatment and tooth extraction. In the present paper, etiology and pathogenesis, differential diagnostic criteria, and diagnostic approaches will be explained and relevant therapeutic principles will be outlined
MasakhaNEWS:News Topic Classification for African languages
African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. While there are individual language specific datasets that are being expanded to different tasks, only a handful of NLP tasks (e.g. named entity recognition and machine translation) have standardized benchmark datasets covering several geographical and typologically-diverse African languages. In this paper, we develop MasakhaNEWS -- a new benchmark dataset for news topic classification covering 16 languages widely spoken in Africa. We provide an evaluation of baseline models by training classical machine learning models and fine-tuning several language models. Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API). Our evaluation in zero-shot setting shows the potential of prompting ChatGPT for news topic classification in low-resource African languages, achieving an average performance of 70 F1 points without leveraging additional supervision like MAD-X. In few-shot setting, we show that with as little as 10 examples per label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of full supervised training (92.6 F1 points) leveraging the PET approach