Improving hierarchical text classification using unlabeled data

Boyapati, Vijay

doi:10.1145/564376.564445

Cited by 9 publications

(9 citation statements)

References 4 publications

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“…A wide range of the supervised learning algorithms has been applied to this area using a training dataset of labelled documents. Common among many of these research efforts is an approach to building hierarchical text classifiers by first subdividing the task into a number of smaller classification tasks -one per decision point in the hierarchy -then building a separate classifier for each of the smaller tasks (Boyapati, 2002). …”

Section: Use Of Ddssa In Topic Modellingmentioning

confidence: 99%

Text classification using document-document semantic similarity

Mukherjee

Mahanti

Bhattacherjee

et al. 2013

IJWS

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Abstract:One of the key problems encountered while using a text classification learning algorithms is that they require huge amount of labelled examples to learn accurately. The objective of this paper is to propose a novel method of topic modelling and document-document semantic similarity algorithm (DDSSA), which reduces the need for larger training data. This algorithm finds the concepts and keywords of the unlabelled text, identifying the topic of unlabelled text from list of concepts and keywords obtained from labelled text. This can be achieved by obtaining the concepts of the labelled text and identify the keywords which holds strong relationships with given labelled data. This topics and keywords obtained from the labelled text can be stored in the database which in turn can be used to compute the semantic similarity with concepts obtained from the unlabelled text. The proposed method is compared with the popular latent semantic analysis (LSA) applied in NLTK and Mallet datasets. The experiment result shows that the proposed method is superior to LSA in most of the cases.

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Section: Use Of Ddssa In Topic Modellingmentioning

confidence: 99%

Text classification using document-document semantic similarity

Mukherjee

Mahanti

Bhattacherjee

et al. 2013

IJWS

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“…Previous work mainly focuses on using machine learning techniques to build text classifiers. Several methods have been proposed in the literature for the construction of document classifiers, such as decision trees [5], Support Vector Machines [13], Bayesian classifiers [24], hierarchical text classifiers [19], [11], [9], [20], [26], [12], [21], [7], [17]. The main commonality in previous methods is that their classification accuracy depends on a training phase, during which statistical techniques are used to learn a model based on a labeled set of training exampled.…”

Section: Related Workmentioning

confidence: 99%

Classifying Web Data in Directory Structures

Stamou¹,

Ntoulas

Krikos³

et al. 2006

Frontiers of WWW Research and Development - APWeb 2006

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Abstract. Web Directories have emerged as an alternative to the Search Engines for locating information on the Web. Typically, Web Directories rely on humans putting in significant time and effort into finding important pages on the Web and categorizing them in the Directory. In this paper, we experimentally study the automatic population of a Web Directory via the use of a subject hierarchy. For our study, we have constructed a subject hierarchy for the top level topics offered in Dmoz, by leveraging ontological content from available lexical resources. We first describe how we built our subject hierarchy. Then, we analytically present how the hierarchy can help in the construction of a Directory. We also introduce a ranking formula for sorting the pages listed in every Directory topic, based on the pages' quality, and we experimentally study the efficiency of our approach against other popular methods for creating Directories.

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“…Systematic organization of information facilitates ease of storage, searching, and retrieval of relevant text content for needy application [9]. The Text Classification is an important technique for organizing text documents into classes [3] [4]. Automatic Text classification is attractive because it relives the organizations from the need of manually organizing document bases, which is not only expensive, time consuming but also error prone [1].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning techniques have the advantage that they are better understood from a theoretical standpoint, leading to performance guarantees and guidance in parameter settings The major machine learning approaches falls under the category of supervised learning, unsupervised learning and semi supervised learning [1] [7]. An increasing number of learning algorithms have been coming day by day, including Regrssion Models , Nearest Neighbor classification, Bayesian probabilistic approaches, Decision Trees , Neural Networks, On-line learning , Support Vector Machine( SVM), Co-training, Expectation Maximization , Graph based methods, Kohenen Self Organizing Maps, etc [3][7] [8].…”

Section: Introductionmentioning

confidence: 99%

Empirical Studies On Machine Learning Based Text Classification Algorithms

Dharmadhikari¹,

Ingle²,

Kulkarni³

2011

ACIJ

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Automatic classification of text documents has become an important research issue now days. Proper classification of text documents requires information retrieval, machine learning and Natural language processing (NLP) techniques. Our aim is to focus on important approaches to automatic text classification based on machine learning techniques viz. supervised, unsupervised and semi supervised. In this paper we present a review of various text classification approaches under machine learning paradigm. We expect our research efforts provide useful insights on the relationships among various text classification techniques as well as sheds light on the future research trend in this domain.

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Improving hierarchical text classification using unlabeled data

Cited by 9 publications

References 4 publications

Text classification using document-document semantic similarity

Text classification using document-document semantic similarity

Classifying Web Data in Directory Structures

Empirical Studies On Machine Learning Based Text Classification Algorithms

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