Automatic assamese text categorization using WordNet

Sarmah, Jumi; Barman, Anup Kumar; Sarma, Shikhar Kumar

doi:10.1109/icacci.2013.6637151

Cited by 11 publications

(8 citation statements)

References 5 publications

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“…There are various emotion analysers for the English language [4][5][6][7][8], but little work has been done in the context of Indian languages [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. The fundamental reason for this is a scarcity of materials in Indian languages.…”

Section: Literaturementioning

confidence: 99%

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A Sentence Level Classification of Telugu News Document using Sentiment Analysis

Bygani,

Venkateshwaralu,

Ramana

2023

E3S Web Conf.

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In recent years, sentiment analysis-based categorization in low-resource languages and regional languages has become a hot topic in natural language processing. Researchers are more interested in categorizing sentiment in Indian languages such as Hindi, Telugu, Tamil, Bengali, Malayalam, and others. To the best of our knowledge, no microscopic study on Indian languages has been published to yet due to a lack of annotated data. Using Telugu sentiment analysis, we presented a two-phase classification technique for Telugu news phrases in this work. It first recognizes subjectivity categorization, which categorizes statements as Positive, Negative, or Neutral. Sentiment Classification is the next step, which divides subjective statements into positive and negative categories. We get an accuracy of 81 percent for sentiment analysis categorization using this method.

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Section: Literaturementioning

confidence: 99%

“…Sarmah, Saharia and Sarma [8] presented an approach for classification of Assamese documents using Assamese WordNet. This approach has accuracy of 90.27 % on Assamese documents.…”

Section: Literaturementioning

confidence: 99%

A Sentence Level Classification of Telugu News Document using Sentiment Analysis

Bygani,

Venkateshwaralu,

Ramana

2023

E3S Web Conf.

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“…In [11] authors have examined decision tree algorithm for Assamese WSD. A sense annotated corpus of 50K sentences for 160 ambiguous words is developed using Assamese Corpus [12] and Assamese WordNet [13] . The context window size of ±2 is used in the disambiguation process.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards Developing Word Sense Disambiguation System for Kashmiri Language

Mir,

Lawaye

2023

SMSJ

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Background: A word, phrase, sentence or other communication is “ambiguous” if interpreted in multiple ways. The process of assigning the correct meaning to a word with respect to its context is known as Word Sense Disambiguation (WSD). WSD is intended to be a very imperious problem in Natural Language Processing (NLP) that requires proper attention as it impacts the performance of various NLP applications.Objectives: In this paper first attempt is made to propose a supervised machine learning Kashmiri WSD system.Material & Methods: The dataset comprising of 500K tokens for this research study has been collected from different resources. A sense annotated corpus for fifty commonly used ambiguous Kashmiri words has been created using the manual annotation method. Kashmiri WordNet is used to extract senses for the target words. Decision-tree based classifier is trained using the features extracted from annotated corpus for carrying out WSD task. We have used context widow of ±3 to extract features that are used to train the classifier.Results: The proposed system is tested on all fifty target words and evaluation is carried using accuracy, precision, recall and F-1 measures. The proposed system reported 81.831% accuracy, 0.834 precision,0.816 recall and 0.824 F1-measure.Conclusions: This was the initial step towards developing the WSD system for Kashmir and it has shown good results. In the future we expect to use other algorithms to carry out this task with greater language coverage

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“…Then the trained classifier model was employed to test the testing text set. The categorization results were assessed using two universal general indexes: precision rate and recall rate [22]. In order to demonstrate the superiority of the proposed method, we compared the experimental results with TF·IDF method.…”

Section: B Feature Selection and Categorizationmentioning

confidence: 99%

A new feature selection method in fishery information processing

2014

2014 10th International Conference on Natural Computation (ICNC)

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Fishery information processing can help fishery researchers obtain the needed information easily and quickly. The current information processing techniques have not solved the problem of high dimensional features in fishery information processing. In this paper, a feature selection method for fishery texts based on SVM-RFE was put forward in view of the characteristics of fishery texts. It removed the redundant information in text feature space and reduced the feature dimensions effectively. Three corpora were employed to verify the proposed method and the comparison with the traditional feature selection method was performed. The experimental results show that the method proposed in this paper can improve precision rate and recall rate of fishery information processing with the lower dimensional features, providing an effective way for fishery information processing.

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Automatic assamese text categorization using WordNet

Cited by 11 publications

References 5 publications

A Sentence Level Classification of Telugu News Document using Sentiment Analysis

A Sentence Level Classification of Telugu News Document using Sentiment Analysis

Towards Developing Word Sense Disambiguation System for Kashmiri Language

A new feature selection method in fishery information processing

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