Rich document representation and classification: An analysis

Keikha, Mostafa; Khonsari, Ahmad; Oroumchian, Farhad

doi:10.1016/j.knosys.2008.06.002

Cited by 27 publications

(13 citation statements)

References 19 publications

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“…The vector space model (VSM) is one of the simplest and most common models for representing documents and is widely used in document classification [75]. In this model, a document is typically represented as bag of words where each word/term is represented as a dimension in a vector space and independent to other terms in the same document.…”

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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“…There are a number of representation techniques that have evolved over through research work done by various researchers in diverse domains. The various data representation models that have been proposed: Bag of Word (BoW) or Vector Space Model (VSM), term weighting approach [4][5], n-grams and nmultigrams approach [6], n-gram graph model [7], keywords or key-phrases approach, Latent Semantic Indexing (LSI) [8], Concise Semantic Analysis (CSA) [9], Rich Data Representation (RDR) [10].…”

Section: Data Representation Modelsmentioning

confidence: 99%

Feature Selection for Effective Text Classification using Semantic Information

Jain¹,

Pise²

2015

IJCA

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Text categorization is the task of assigning text or documents into pre-specified classes or categories. For an improved classification of documents text-based learning needs to understand the context, like humans can decide the relevance of a text through the context associated with it, thus it is required to incorporate the context information with the text in machine learning for better classification accuracy. This can be achieved by using semantic information like part-of-speech tagging associated with the text. Thus the aim of this experimentation is to utilize this semantic information to select features which may provide better classification results. Different datasets are constructed with each different collection of features to gain an understanding about what is the best representation for text data depending on different types of classifiers.

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“…O problema de capturar a dependência entre os termos de forma eficiente para incorporarà representação dos documentos, aindaé um grande desafio de pesquisa, e tem despertado atenção recentemente (Keikha et al, 2009;Figueiredo et al, 2011;Farahat and Kamel, 2011;Kalogeratos and Likas, 2012;Cheng et al, 2013b;Gao et al, 2013). Além disso, diversos modelos para representação de documentos foram propostos na literatura para capturar a dependência entre os termos, como discutido na Seção 3.1.3.…”

Section: Extração De Tópicos Com Termos Dependentes Para Representaçãunclassified

“…Uma vez estruturados os documentos, algoritmos de mineração de dados convencionais podem ser aplicados para extrair conhecimento e informação por meio de padrões detectados em toda a coleção de documentos (Aggarwal and Zhai, 2012;Rios, 2013). Nesse contexto, a qualidade dos resultados obtidos com as abordagens automáticas para obtenção de conhecimento de textos estão fortemente relacionadosà qualidade dos atributos utilizados para representar a coleção de documentos (Shafiei et al, 2007;Keikha et al, 2009;Aggarwal and Zhai, 2012).…”

Section: Introductionunclassified

“…Existem muitas evidências na literatura de que representações que incorporam alguma informação de relação entre termos melhora os resultados de tarefas como recuperação de informação (Wong et al, 1985;Billhardt et al, 2002;Pôssas et al, 2002), classificação de textos (Lewis, 1992;Keikha et al, 2009;Figueiredo et al, 2011), agrupamento de textos (Beil et al, 2002;Zhang et al, 2010;Rossi and Rezende, 2011;Marcacini et al, 2012a;Kalogeratos and Likas, 2012;Cheng et al, 2013b) ou extração de tópicos (Wallach, 2006;Wu et al, 2010;Zhu et al, 2012;Lau et al, 2013). Diversos modelos para representação dos documentos foram propostos na literatura visando capturar a informação de relação entre termos, destacando-se os modelos baseados em frases ou termos compostos (Pôssas et al, 2002;Figueiredo et al, 2011;Rossi and Rezende, 2011), o Modelo de Espaço de Vetores Generalizado 6 (MEVG) e suas extensões (Wong et al, 1985;Billhardt et al, 2002;Farahat and Kamel, 2011;Kalogeratos and Likas, 2012;Cheng et al, 2013b), modelos de tópicos não-probabilísticos, como o Latent Semantic Analysis (LSA) (Deerwester et al, 1990) ou o Non-negative Matrix Factorization (NMF) (Lee and Seung, 1999) e modelos de tópicos probabilísticos e suas extensões (Hofmann, 1999;Blei et al, 2003;Wallach, 2006;Kim et al, 2012;Zhu et al, 2012;Lau et al, 2013;Gao et al, 2013).…”

Rich document representation and classification: An analysis

Cited by 27 publications

References 19 publications

A Hybrid Concept Learning Approach to Ontology Enrichment

A Hybrid Concept Learning Approach to Ontology Enrichment

Feature Selection for Effective Text Classification using Semantic Information

Extração de tópicos baseado em agrupamento de regras de associação

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