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Related Concept Videos

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Naming Acid Halides
The IUPAC and common names of acid halides are derived from the corresponding carboxylic acids, by changing “ic acid” to “yl halide.” For example, as shown below, the IUPAC name ethanoyl chloride is derived from ethanoic acid, and the common name, acetyl chloride, is obtained from acetic acid.
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IUPAC Nomenclature of Ketones01:09

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Like aldehydes, ketones are named using IUPAC rules; in this case, by replacing “e” in the name of the longest hydrocarbon chain with “one.” In acyclic ketones, the ketonic carbon is given the lowest locant value. For instance, as shown below, a simple five-carbon ketone is named pentan-2-one, instead of pentan-4-one. IUPAC rules also allow the placing of the locant value before the parent name to give an alternate name, 2-pentanone.
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In the late 19th-century, the number of new chemical compounds discovered increased tremendously. Hence, the necessity arose to develop a naming system for the systematic nomenclature of these newly discovered compounds. IUPAC (International Union for Pure and Applied Chemistry), established in 1919, sets rules for the nomenclature.
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When more than one substituent is present on the benzene ring, the IUPAC nomenclature depends on the number of substituents present.
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Nomenclature of Alkenes02:29

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The IUPAC naming system for alkenes replaces -an- with -en- in the corresponding parent alkanes. Accordingly, a simple alkene replaces the -ane suffix of the alkane with -ene.
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Related Experiment Video

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Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
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Lexicon dataset for the Hausa language.

Idi Mohammed1, Rajesh Prasad1

  • 1Computer Science Department, African University of Science and Technology, Abuja, Nigeria.

Data in Brief
|February 15, 2024
PubMed
Summary

Researchers created a new sentiment analysis dataset for the Hausa language. This augmented lexicon resource aids natural language processing for low-resource languages like Hausa.

Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • African Languages

Background:

  • Sentiment analysis research often lacks resources for low-resource languages.
  • The Hausa language presents unique challenges due to limited digital data.

Purpose of the Study:

  • To develop and present a comprehensive sentiment analysis dataset for the Hausa language.
  • To address the scarcity of labeled data for Hausa sentiment analysis.

Main Methods:

  • Constructed an augmented lexicon using a Hausa dictionary.
  • Applied data augmentation techniques to expand the dataset size.
  • Manually annotated data for sentiment polarity (positive, negative, neutral).

Main Results:

Keywords:
Data augmentationLexicon-basedLow-resource languagesSentiment analysisSocial media

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  • Created a dataset with 14,663 entries: 4,154 positive, 4,310 negative, and 6,199 neutral.
  • The dataset provides a balanced representation of sentiment polarities.
  • Successfully augmented a lexicon-based dataset for sentiment analysis.

Conclusions:

  • The developed Hausa sentiment analysis dataset is a valuable resource for NLP research.
  • This dataset will facilitate the development of sentiment analysis models for Hausa social media and product reviews.
  • Contributes significantly to the field of low-resource language sentiment analysis.