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Benzene is the simplest aromatic hydrocarbon or arene. The IUPAC names for simple monosubstituted benzene derivatives are derived by adding the substituent's name as a prefix to the parent benzene. For example, halobenzene, where the halogen could be fluoro (F), chloro (Cl), bromo (Br), and iodo (I).
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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.
For disubstituted benzene derivatives, with two groups attached to the benzene ring, three constitutional isomers are possible. For example, consider dimethyl benzene, often called xylene, where the second methyl group can be substituted at the second, third, or fourth carbon. The relative position of the substituents is represented by prefixes ortho, meta, or...
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The secondary and tertiary amines are derivatives of ammonia, where two and three of its hydrogens are replaced by alkyl groups, respectively. Secondary and tertiary amines can be symmetrical with identical alkyl groups attached to the nitrogen atom or unsymmetrical when more than one type of alkyl group is present. The standard nomenclature of secondary and tertiary amines is similar to the names given to the primary amines. They are generally named alkylamines. As depicted in Figure 1, for...
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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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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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The simplest aromatic amine is phenylamine, which contains an –NH2 functionality directly attached to an aromatic ring. The name aniline is designated for this skeleton. As shown in Figure 1, the common names of the functionalized anilines involve prefixes ortho-, meta-, and para- to indicate the substitution position. Different functionalized aniline derivatives also have notable trivial names.
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DarNERcorp: An annotated named entity recognition dataset in the Moroccan dialect.

Hanane Nour Moussa1, Asmaa Mourhir1

  • 1School of Science and Engineering, Al Akhawayn University in Ifrane, P.O. Box 104, Hassan II Avenue, Ifrane 53000, Morocco.

Data in Brief
|June 29, 2023
PubMed
Summary
This summary is machine-generated.

DarNERcorp is a new dataset for Named Entity Recognition (NER) in Moroccan Darija. This resource aids Arabic Natural Language Processing (NLP) by providing annotated data for dialectal and mixed Arabic.

Keywords:
BIOCorpusDialectal ArabicNamed entity recognitionNatural language processingText mining

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Corpus Linguistics

Background:

  • Annotated corpora are crucial for developing robust Natural Language Processing (NLP) systems.
  • Existing Arabic NLP resources often lack sufficient dialectal data, particularly for Moroccan Darija.
  • Named Entity Recognition (NER) is a fundamental NLP task requiring high-quality annotated datasets.

Purpose of the Study:

  • To introduce DarNERcorp, a novel manually annotated dataset for Named Entity Recognition (NER) in Moroccan dialect (Darija).
  • To address the scarcity of annotated corpora for dialectal Arabic, specifically for NLP applications.
  • To provide a valuable resource for training and evaluating NER systems for Darija and mixed Arabic.

Main Methods:

  • Data collection involved scraping content from the Moroccan Dialect section of Wikipedia.
  • Manual annotation of 65,905 tokens using the BIO (Beginning, Inside, Outside) tagging scheme.
  • Utilized open-source libraries and tools for data processing and annotation.

Main Results:

  • The DarNERcorp dataset comprises 65,905 tokens with BIO tags.
  • 13.8% of the tokens are identified as named entities across four categories: person, location, organization, and miscellaneous.
  • The dataset provides a comprehensive resource for dialectal Arabic NER.

Conclusions:

  • DarNERcorp represents a significant contribution to the Arabic NLP community by filling a gap in dialectal annotated corpora.
  • The dataset enables the development and benchmarking of NER systems for Moroccan Darija.
  • It facilitates advancements in processing and understanding mixed and dialectal Arabic text.