Analysis of BMW Model for Title Word Selection on Indic Script

Reddy, P. Vijayapal; Vardhan, B. Vishnu; Govardhan, A.

doi:10.5120/2304-2915

Cited by 4 publications

(2 citation statements)

References 7 publications

(4 reference statements)

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“…As described by (Reddy et al, 2011a;2011b), a stop list can be a list of ordinarily perennial features that appear in a text document. Standard features such as conjunctions, prepositions and pronouns should be deleted to prevent their results from having an impact on the classification.…”

Section: Stop-word Removalmentioning

confidence: 99%

Arabic Poetry Authorship Attribution using Machine Learning Techniques

Ahmed¹,

Ramdani²,

Bellafkih³

2019

Journal of Computer Science

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In this study, authorship attribution in Arabic poetry will be conducted to determine the authorship of a specified text after documents with recognized authorships have been allocated. This work also measures the impact performance of Naïve Bayes, Support Vector Machine and Linear discriminant analysis for Arabic poetry authorship attribution using text mining classification. Several features such as lexical features, character features, structural features, poetry features, syntactic features, semantic features and specific word features are utilized as the input data for text mining, using classification algorithms Linear discriminant analysis, Support Vector Machine and Naïve Bayes by Arabic Poetry Authorship Attribution Model (APAAM). The dataset of Arabic poetry is divided into two sets: known poetic in training dataset texts and anonymous poetic texts in a test dataset part. In the experiment, a set of 114 random poets from entirely different eras are used. The highest performance accuracy value is 99, 12%; the performance rate at the attribute level is 98.246%; the level of techniques is 92.836%.

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Section: Stop-word Removalmentioning

confidence: 99%

Arabic Poetry Authorship Attribution using Machine Learning Techniques

Ahmed¹,

Ramdani²,

Bellafkih³

2019

Journal of Computer Science

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“…They stated that by using text summarization after searching the document they save the user's time required for reading the complete document. P.Vijayapal Reddy et al 2011 [3] investigated the problem of title word selection in the process of title generation for a given text document by using BMW approach. They stated that they tried to explore the impact of word weigh on Title word selection by using BMW model.…”

Section: Introductionmentioning

confidence: 99%

Text Summarization using Centrality Concept

Al-Gaphari¹,

Ba-Alwi²,

Moharram³

2013

IJCA

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ABSTRACT:The amount of textual information available on the web is estimated by terra bytes. Therefore constructing a software program to summarize web pages or electronic documents would be a useful technique. Such technique would speed up of reading, information accessing and decision making process. This paper investigates a graph based centrality algorithm on Arabic text summarization problem (ATS). The graph based algorithm depends on extracting the most important sentences in a documents or a set of documents (cluster). The algorithm starts computing the similarity between two sentences and evaluating the centrality of each sentence in a cluster based on centrality graph. Then the algorithm extracts the most important sentences in the cluster to include them in a summary. The algorithm is implemented and evaluated by human participants and by an automatic metrics. Arabic NEWSWIRE-a corpus is used as a data set in the algorithm evaluation. The result was very promising. General Terms:AI Applications, NLP, Text Mining and AI Algorithms

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Influence of Lexical, Syntactic and Structural Features and their Combination on Authorship Attribution for Telugu Text

Prasad

Narsimha

Reddy³

et al. 2015

Procedia Computer Science

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Authorship attribution (AA) is the task of identifying author of an unknown text from the known author set. Authorship Attribution can be viewed is a problem of text classification. AA is based on the classification of documents on author writing style rather than the topic of the text. In this paper experimental evaluations were carried out on Telugu text for Authorship Attribution using various types of features and their combinations. Feature vectors were formed for the training set using lexical, syntactic and structural features and their combinations. Learned model was generated for each these vectors and performance of the learned model is calculated using F1 metric and accuracy. More number of features can slow down the model performance. Features which are not relevant or not more relevant were eliminated from the feature vectors using chi-square metric. Support Vector Machine (SVM) algorithm is used as a classifier to generate the learned model for each dimensional feature vector. This learned model is used to assign the anonymous text to one of the known authors.

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Analysis of BMW Model for Title Word Selection on Indic Script

Cited by 4 publications

References 7 publications

Arabic Poetry Authorship Attribution using Machine Learning Techniques

Arabic Poetry Authorship Attribution using Machine Learning Techniques

Text Summarization using Centrality Concept

Influence of Lexical, Syntactic and Structural Features and their Combination on Authorship Attribution for Telugu Text

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