Classification of Gujarati Documents using Naïve Bayes Classifier

Rakholia, Rajnish M.; Saini, Jatinderkumar R.

doi:10.17485/ijst/2017/v10i5/103233

Cited by 23 publications

(4 citation statements)

References 15 publications

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“…The maximum accuracy 56%, 54%, 44%, 64%, and 52% using Random Forest, KNN, Decision Tree, Multinomial Naïve Bayes, SVM, and Gausian Naïve Bayes. For the first time, Rakholia [17] classified Gujrati documents using Naïve Bayes method with and without feature selection which obtained 75.74% and 88.96% accuracy respectively. As on date, there is no document classifier available for Pashto language.…”

Section: Related Workmentioning

confidence: 99%

Tuning Language Processing Approaches for Pashto Texts Classification

Baktash¹,

Dawodi²

2023

IJCI

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Nowadays, text classification for different purposes becomes a basic task for concerned people. Hence, much research has been done to develop automatic text classification for the majority of national and international languages. However, the need for an automated text classification system for local languages is felt. The main purpose of this study is to establish a novel automatic classification system of Pashto text. In order to follow this up, we established a collection of Pashto documents and constructed the dataset. In addition, this study includes several models containing statistical techniques and neural network neural machine learning including DistilBERT-base-multilingual-cased, Multilayer Perceptron, Support Vector Machine, K Nearest Neighbor, decision tree, Gaussian naïve Bayes, multinomial naïve Bayes, random forest, and logistic regression to discover the most effective approach. Moreover, this investigation evaluates two different feature extraction methods including bag of words, and Term Frequency Inverse Document Frequency. Subsequently, this research obtained an average testing accuracy rate of 94% using the MLP classification algorithm and TFIDF feature extraction method in single label multi-class classification. Similarly, MLP+TFIDF with F1-measure of 0.81 showed the best result. Experiments on the use of pre-trained language representation models (such as DistilBERT) for classifying Pashto texts show that we need a specific tokenizer for a particular language to obtain reasonable results.

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Section: Related Workmentioning

confidence: 99%

Tuning Language Processing Approaches for Pashto Texts Classification

Baktash¹,

Dawodi²

2023

IJCI

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“…They combined GSS [8] coefficients and TF-IDF for extracting key words for summarization. Rakholia et al [9] proposed a system wherein Naïve Bayes (NB) statistical machine learning algorithm is used along with TF-IDF approach to classify Gujarati documents. As seen from the literature review, TF-IDF is an important technique used by researchers for document classification as well as language processing tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Term Frequency - Inverse Document Frequency to find the Relevance of Words in Gujarati Language

Dodiya¹

2021

IJRASET

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TF-IDF, term frequency-inverse document frequency, is used to evaluate the relevance of a word to a document in a collection. It is used in information retrieval and text mining field. In this technique, the weight of the word is calculated which signifies the importance of the word in the document. This paper discusses about the approach to determine TF-IDF for documents in Gujarati language, which is a morphologically rich Indo-Aryan language.The approach calculates TF-IDF, based on the method to find the frequency of the words in the document. The system shows significant results, which is discussed in detail.

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“…They applied an automatic and dynamic approach to identify stop words from Gujarati documents and claimed 94.08% average accuracy. www.ijacsa.thesai.org Research works involving Natural Language Processing (NLP) of Gujarati language have been presented for MTS for Sanskrit-Gujarati pair [18], comparison of morphologically analyzed words [19], bilingual dictionary implementation [20], constituency mapper [21], classification [22] and information retrieval [23] to name a few.…”

Section: Related Literature Reviewmentioning

confidence: 99%

Dynamic Phrase Generation for Detection of Idioms of Gujarati Language using Diacritics and Suffix-based Rules

Modh¹,

Saini²

2021

IJACSA

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Gujarati is the language used for everyday communication in the state of Gujarat, India. The Gujarati language is also officially recognized by the constitution and the government of India. Gujarati script is based on the Devanagari script. An idiom is an expression, phrase, or word that has a different meaning from the literal meaning of the words in it. Idioms represent the cultural heritage of Gujarati language. Idioms are used in Gujarati language for effective communication and convey of an accurate message. No Machine Translation System does the accurate translation of Gujarati idioms to English or any other language. Different idiom phrases can be generated by adding diacritic(s) as well as suffix to the root or base form of the idiom. Many forms of single idiom make automatic idiom identification as well as machine translation more challenging. This paper focuses on the design and implementation of diacritics and suffix-based rules for dynamic phrase generation and detection of idioms of Gujarati language. This implementation helps in identifying Gujarati idiom present in any possible form in the Gujarati text. The obtained results with the execution of 7050 different Gujarati idiom phrases yield an accuracy of 99.73%. The results are encouraging enough to make the proposed implementation useful for Natural Language processing tasks related to Gujarati language idioms.

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Classification of Gujarati Documents using Naïve Bayes Classifier

Cited by 23 publications

References 15 publications

Tuning Language Processing Approaches for Pashto Texts Classification

Tuning Language Processing Approaches for Pashto Texts Classification

Using Term Frequency - Inverse Document Frequency to find the Relevance of Words in Gujarati Language

Dynamic Phrase Generation for Detection of Idioms of Gujarati Language using Diacritics and Suffix-based Rules

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