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Sentence Classification Using N-Grams in Urdu Language Text

Authors :
Sikandar Ali
Malik Daler Ali Awan
Ali Samad
Nadeem Iqbal
Malik Muhammad Saad Missen
Niamat Ullah
Source :
Scientific Programming, Vol 2021 (2021)
Publication Year :
2021
Publisher :
Hindawi Limited, 2021.

Abstract

The usage of local languages is being common in social media and news channels. The people share the worthy insights about various topics related to their lives in different languages. A bulk of text in various local languages exists on the Internet that contains invaluable information. The analysis of such type of stuff (local language’s text) will certainly help improve a number of Natural Language Processing (NLP) tasks. The information extracted from local languages can be used to develop various applications to add new milestone in the field of NLP. In this paper, we presented an applied research task, “multiclass sentence classification for Urdu language text at sentence level existing on the social networks, i.e., Twitter, Facebook, and news channels by using N-grams features.” Our dataset consists of more than 1,00000 instances of twelve (12) different types of topics. A famous machine learning classifier Random Forest is used to classify the sentences. It showed 80.15%, 76.88%, and 64.41% accuracy for unigram, bigram, and trigram features, respectively.

Details

ISSN :
1875919X and 10589244
Volume :
2021
Database :
OpenAIRE
Journal :
Scientific Programming
Accession number :
edsair.doi.dedup.....fe6be4684cd459af3b4e51d0a0d44dfd