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Contextual Urdu Lemmatization Using Recurrent Neural Network Models

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AA8WG8HLJ" target="_blank" >RIV/00216208:11320/23:A8WG8HLJ - isvavai.cz</a>

  • Result on the web

    <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147741458&doi=10.3390%2fmath11020435&partnerID=40&md5=8c8e80f7449efb5660ee1f506b9f5ae9" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147741458&doi=10.3390%2fmath11020435&partnerID=40&md5=8c8e80f7449efb5660ee1f506b9f5ae9</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/math11020435" target="_blank" >10.3390/math11020435</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Contextual Urdu Lemmatization Using Recurrent Neural Network Models

  • Original language description

    "In the field of natural language processing, machine translation is a colossally developing research area that helps humans communicate more effectively by bridging the linguistic gap. In machine translation, normalization and morphological analyses are the first and perhaps the most important modules for information retrieval (IR). To build a morphological analyzer, or to complete the normalization process, it is important to extract the correct root out of different words. Stemming and lemmatization are techniques commonly used to find the correct root words in a language. However, a few studies on IR systems for the Urdu language have shown that lemmatization is more effective than stemming due to infixes found in Urdu words. This paper presents a lemmatization algorithm based on recurrent neural network models for the Urdu language. However, lemmatization techniques for resource-scarce languages such as Urdu are not very common. The proposed model is trained and tested on two datasets, namely, the Urdu Monolingual Corpus (UMC) and the Universal Dependencies Corpus of Urdu (UDU). The datasets are lemmatized with the help of recurrent neural network models. The Word2Vec model and edit trees are used to generate semantic and syntactic embedding. Bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), bidirectional gated recurrent neural network (BiGRNN), and attention-free encoder–decoder (AFED) models are trained under defined hyperparameters. Experimental results show that the attention-free encoder-decoder model achieves an accuracy, precision, recall, and F-score of 0.96, 0.95, 0.95, and 0.95, respectively, and outperforms existing models. © 2023 by the authors."

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    "Mathematics"

  • ISSN

    2227-7390

  • e-ISSN

  • Volume of the periodical

    11

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    20

  • Pages from-to

    1-20

  • UT code for WoS article

    000927195600001

  • EID of the result in the Scopus database

    2-s2.0-85147741458