A novel semantic smoothing kernel for text classification with class-based weighting

Altınel, Berna; Di̇ri̇, Banu; Ganiz, Murat Can

doi:10.1016/j.knosys.2015.07.008

Cited by 17 publications

(29 citation statements)

References 53 publications

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“…Even though vector space document representation model has proved to be very simple and commonly used in the domain of text classification, it however has some limitations [6,7,150]. The main limitation of this model is the ignorance of dependencies of terms, i.e.…”

Section: Background and Motivationmentioning

confidence: 99%

A Hybrid Concept Learning Approach to Ontology Enrichment

Kastrati

Imran

Yayilgan

2018

Innovations, Developments, and Applications of Semantic Web and Information Systems

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Declaration of AuthorshipI, Zenun Kastrati, hereby declare that this thesis and the work presented in it is entirely my own. Where I have consulted the work of others, this is always clearly stated.Signed:(Zenun Kastrati) Date: SummaryWe are living in the age of internet where massive amount of information is produced from various digital resources on daily basis. The information of these resources is typically stored in unstructured textual format such as reports, news, e-mails, blogs, etc., therefore, a proper classification and organization of this huge amount of information is apparently needed. In this regard, an automatic classification, particularly ontology-based classification, plays an important role in helping people to classify and organize the information accordingly. The ontology-based classification system is an automatic system that utilizes the ontology in order to take advantages of organizing and classifying the knowledge in a more structural and formal way, thus providing better classification accuracy comparing to the traditional keyword-based classification system.The performance of an ontology-based document classification system can be affected by several aspects involved in the entire classification process that generally is constituted of steps such as document collection and preprocessing, document representation, dimensionality reduction, and the classifier. It is almost impossible to address all these research aspects in order to obtain performance improvement in a single dissertation research work, therefore we selected to work on the aspects that we consider are either rarely studied or have a crucial role on the ontology-based classification system. Document representation is one of the main aspects that affects the performance of ontology-based document classification, thus the first research aspect that we investigated is enriching document representation with semantics utilizing the background knowledge exploited by ontologies. The background knowledge derived from an ontology is embedded in a document using a matching technique. The idea behind this technique is mapping of terms that occur in a document with the relevant ontology concepts by searching only the presence of concepts labels in that document. Searching only the presence of concepts labels occurring in a document limits the capabilities of the classification system to capture and exploit the entire conceptualization involved in that document due to the semantic gap issue, the lack of an in depth-coverage of concepts, and the ambiguity problem. In this thesis, the focus is placed on the conceptual document representation, in which, a document is associated with a set of concepts not only by looking for the appearance of concept labels, but also through the acquisition of lexical information integrated (linked) to the ontology to enriching its coverage with new concepts. In this respect, an automatic ontology concept enrichment model is developed to enrich ontologies with new concepts in order to provide a broader co...

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Section: Background and Motivationmentioning

confidence: 99%

A Hybrid Concept Learning Approach to Ontology Enrichment

Kastrati

Imran

Yayilgan

2018

Innovations, Developments, and Applications of Semantic Web and Information Systems

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“…The work in [19] proposed a text feature selection method based on "TongYiCi Cilin" to reduce data's feature dimensions while ensuring data integrity and classification accuracy. A semantic kernel is used with SVM for text classification to improve the accuracy in [20,21]. What is more, there are some other work in [22][23][24] that presents some excellent ideas, which is worth learning and reference when we are dealing with large-scale text classification.…”

Section: Synonym Mergingmentioning

confidence: 99%

A feature selection method based on synonym merging in text classification system

Liu

Zhang

et al. 2017

J Wireless Com Network

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As an important step in natural language processing (NLP), text classification system has been widely used in many fields, like spam filtering, news classification, and web page detection. Vector space model (VSM) is generally used to extract feature vectors for representing texts which is very important for text classification. In this paper, a feature selection algorithm based on synonym merging named SM-CHI is proposed. Besides, the improved CHI formula and synonym merging are used to select feature words so that the accuracy of classification can be improved and the feature dimension can be reduced. In addition, for feature words selected by SM-CHI, this paper presented three weight calculation algorithms to explore the best feature weight update method. Finally, we designed three comparative experiments and proved the classification accuracy is the highest when choosing the improved CHI formula 2, set the threshold α to 0.8 and use the largest weight among the synonyms to update the feature weight, respectively.

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“…Different choices of matrix S lead to different variants of semantic kernels, such as latent semantic kernel [9], domain kernel [11] and class weighting kernel [14].…”

Section: Exponential Kernelmentioning

confidence: 99%

“…POLITICS) and a set of hand tagged examples is provided for training. Pioneered by [3], kernel methods [4] such as support vector machine (SVM) [5] have been heavily used for text categorization tasks, typically showing good results [6][7][8][9][10][11][12][13][14][15][16][17]. Basically, kernel methods work by mapping the data from the input space into a high-dimensional (possibly infinite) feature space, which is usually chosen to be a reproducing kernel Hilbert space (RKHS), and then building linear algorithms in the feature space to implement nonlinear counterparts in the input space.…”

Section: Introductionmentioning

confidence: 99%

“…For example: the semantic kernels that use the external semantic knowledge like WordNet and Wikipeida were proposed to improve the kernel-based text categorization systems [6][7][8]. In the absence of external semantic knowledge, corpusbased statistical approaches are applied to capture the semantic relations between terms, resulting in the corpus-based semantic kernels [9][10][11][12][13][14]. Besides these kernels which are only suitable for vectorial types of data, the kernels suitable for non-vectorial types of data were also proposed for text categorization, such as string kernel [15], word-sequence kernel [16] and tree kernel [17].…”

Section: Introductionmentioning

confidence: 99%

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