Summary of Text Categorization Can Enhance Domain-agnostic Stopword Extraction, by Houcemeddine Turki et al.
Text Categorization Can Enhance Domain-Agnostic Stopword Extraction
by Houcemeddine Turki, Naome A. Etori, Mohamed Ali Hadj Taieb, Abdul-Hakeem Omotayo, Chris Chinenye Emezue, Mohamed Ben Aouicha, Ayodele Awokoya, Falalu Ibrahim Lawan, Doreen Nixdorf
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this study, researchers explored how text categorization can help streamline stopword extraction in natural language processing (NLP) for nine African languages, including French. By leveraging specific datasets, they found that text categorization effectively identifies domain-agnostic stopwords with high detection rates for most examined languages, although linguistic variances led to lower detection rates for certain languages. The study highlights the importance of combining statistical and linguistic approaches to create comprehensive stopword lists, which enhances NLP for African languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make computers better at understanding texts from Africa by helping them find unimportant words, like “the” or “and”. Researchers used special collections of text to see if they could get computers to correctly identify these words. They found that using a specific method called “text categorization” helps computers do this job well for most African languages. But the researchers also saw that different languages have their own special patterns, which makes it harder for computers to understand them. By combining two ways of doing things – one based on numbers and one based on language rules – the researchers created a better way to make these lists of unimportant words. |
Keywords
* Artificial intelligence * Natural language processing * Nlp * Stopword
