Trends in Document Analysis

Khatavkar, Vaibhav; Kulkarni, Parag

doi:10.1007/978-981-13-1402-5_19

Cited by 6 publications

(7 citation statements)

References 27 publications

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“…In [7], Khatavkar and Kulkarni explain various trends in document analysis in which vectorization of data is focused. However, this is not sufficient to select a particular algorithm.…”

Section: Nearest Neighborsmentioning

confidence: 99%

Multi-Perspective Analysis of News Articles using Machine Learning Algorithms

Khatavkar¹,

Velankar²,

Kulkarni³

2019

IJCA

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Nowadays, many machine learning algorithms are evolving. It is a very difficult task to select a particular algorithm for a specific problem. A multi-perspective analysis of the given input data has to be performed to select a particular algorithm. In this study a case study has been taken for selecting an algorithm for the classification of news articles. Multiperspective analysis is performed on the data using various machine learning algorithms namely Random Forest Classifier, Decision tree, AdaBoostClassifier, SVM with Linear SVC and SVM with NuSVC. For the multi perspective analysis, features from the dataset are extracted and standard metrics are used. The metrics used are Kappa, Accuracy, Fmeasure, Recall, and Precision. For the BBC news standard dataset, SVM Linear SVC proves to be effective because its classification rate is 96% and false positive rate is 0.75%.

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“…In [7], Khatavkar and Kulkarni explain various trends in document analysis in which vectorization of data is focused. However, this is not sufficient to select a particular algorithm.…”

Section: Nearest Neighborsmentioning

confidence: 99%

Multi-Perspective Analysis of News Articles using Machine Learning Algorithms

Khatavkar¹,

Velankar²,

Kulkarni³

2019

IJCA

Self Cite

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“…Typically, Latent Semantic Analysis (LSA) is used for theme generation. Khatavkar V and Kulkarni P in [2], devised an algorithm to get the context of a document that gave context terms from the document and their weights. Khatavkar V and Kulkarni P in [3], compared SVM with LSA and without LSA.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Context Vector Machine: Researchers in [2,5] derived using context using Term Frequency Inverse Document Frequency (TF-IDF). The work presented in this paper is an extension of the work mentioned in [8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The F-measure of the eCVM can be compared to that of existing system in [2,16]. The comparison of the proposed system with other methods of context derivation is done in Table 4.…”

Section: 5%mentioning

confidence: 99%

“…The context derived in context vector machine [5] and LSA [2] give context as various terms which define a single context using single document or paragraph. We combine them along with named entities to improve the performance of the system.…”

Section: Introductionmentioning

confidence: 99%

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Thematic Context Derivator Algorithm for Enhanced Context Vector Machine: eCVM

Khatavkar,

Velankar,

Kulkarni

2019

IJEAT

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Natural Language Processing uses word embeddings to map words into vectors. Context vector is one of the techniques to map words into vectors. The context vector gives importance of terms in the document corpus. The derivation of context vector is done using various methods such as neural networks, latent semantic analysis, knowledge base methods etc. This paper proposes a novel system to devise an enhanced context vector machine called eCVM. eCVM is able to determine the context phrases and its importance. eCVM uses latent semantic analysis, existing context vector machine, dependency parsing, named entities, topics from latent dirichlet allocation and various forms of words like nouns, adjectives and verbs for building the context. eCVM uses context vector and Pagerank algorithm to find the importance of the term in document and is tested on BBC news dataset. Results of eCVM are compared with compared with the state of the art for context detrivation. The proposed system shows improved performance over existing systems for standard evaluation parameters.

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