Automatic classification of Tamil documents using vector space model and artificial neural network

Rajan, K.; Ramalingam, V.; Ganesan, M.; Palanivel, S.; Palaniappan, B.

doi:10.1016/j.eswa.2009.02.010

Cited by 59 publications

(10 citation statements)

References 10 publications

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“…A neural network model is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information (Liu, Yuan, & Liao, 2009;Rajan, Ramalingam, Ganesan, Palanivel, & Palaniappan, 2009). Neural network is composed of artificial neurons or nodes.…”

Section: Neural Networkmentioning

confidence: 99%

Exploring the risk factors of preterm birth using data mining

Chen

Chuang

Yang

et al. 2011

Expert Systems with Applications

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Section: Neural Networkmentioning

confidence: 99%

Exploring the risk factors of preterm birth using data mining

Chen

Chuang

Yang

et al. 2011

Expert Systems with Applications

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“…Rajan, Ramalingam, Ganesan, Palanivel and Palaniappan [16] developed Tamil text classification system based on Vector Space Model (VSM) and Neural Network (NN) model. They applied inflectional rules to reduce number of terms.…”

Section: Related Workmentioning

confidence: 99%

Automatic Marathi Text Classification

Patil*¹,

Bhavsar²,

Pawar³

2019

IJITEE

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Multifold growth of internet users due to penetration of Information and Communication technology has resulted in huge soft content on the internet. Though most of it is available in English language, other languages including Indian languages are also catching up the race rapidly. Due to exponential growth in Internet users in India common man is also posting moderate size data on the web. Due to which e-content in Indian languages is growing in size. This high dimensionality of e-content is a curse for Information Retrieval. Hence automatic text classification and structuring of this e-content has become the need of the day. Automatic text classification is the process of assigning a category or categories to a new test document from one or more predefined categories according to the contents of that document. Text classification works for 14 Indian languages are reported in the literature. Marathi language is one of the officially recognized languages of Indian union. Little work has been done for Marathi text classification. This paper investigates Marathi text classification using popular Machine Learning methods such as Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, Centroid Based and Modified KNN (MKNN) on manually extracted newspaper data from sport’s domain. Our experimental results show that Naïve Bayes and Centroid Based give best performance with 99.166% Micro and Macro Average of F-score and Modified KNN gives lowest performance with 97.16% Micro Average of F-Score and 96.997% Macro Average of F-score. The proposed work will be helpful for proper organization of Marathi text document and many applications of Marathi Information Retrieval.

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“…Distribution of concurrent events shows the importance of a word in its document. [9] employs the two methods of vector space model and artificial nerve system with 1000L-25N51 structure to analyze, distinguish and classify documents. This work merits from frequency of highly-repetitive words, TF and IDF parameters, and the Euclidean distance between documents.…”

Section: Introductionmentioning

confidence: 99%

Specialization of keyword extraction approach to Persian texts

Khozani

Bayat

2011

2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR)

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As the amount of data increases and the relations among them get more complex, access to information implicit in data appears more difficult, and the role of methods of getting data from diverse texts, and analyzing them becomes more significant. Of such methods is the highly effective technique of keyword extraction which shows the concept and content of the original text. In this article, a new approach is presented with the aim of extracting keywords with respect to combined words, and extracting key sentences in Persian documents so as to classify them efficiently. Studies performed on several Persian documents, and comparisons done between the findings of these and other methods have proven that this method extracts keywords of texts with much more accuracy and speed to represent the original concepts.

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Automatic classification of Tamil documents using vector space model and artificial neural network

Cited by 59 publications

References 10 publications

Exploring the risk factors of preterm birth using data mining

Exploring the risk factors of preterm birth using data mining

Automatic Marathi Text Classification

Specialization of keyword extraction approach to Persian texts

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