A semantic term weighting scheme for text categorization

Luo, Qinghua; Chen, Enhong; Xiong, Hui

doi:10.1016/j.eswa.2011.04.058

Cited by 83 publications

(60 citation statements)

References 12 publications

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“…These techniques use eigenvectors in different variations and apply them on statistical procedures. (Jiang, Berry, Donato, Ostrouchov, & Grady, 1999;Luo, Chen, & Xiong, 2011). With these techniques, words that occur in project or technology descriptions are used in the hidden semantic patterns but also words, that might be in these descriptions (Thorleuchter & Van den Poel, 2012d).…”

Section: Semantic Approachesmentioning

confidence: 99%

Technology classification with latent semantic indexing

Thorleuchter

Poel

2013

Expert Systems with Applications

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Many national and international governments establish organizations for applied science research funding. For this, several organizations have defined procedures for identifying relevant projects that based on prioritized technologies. Even for applied science research projects, which combine several technologies it is difficult to identify all corresponding technologies of all research-funding organizations. In this paper, we present an approach to support researchers and to support research-funding planners by classifying applied science research projects according to corresponding technologies of research-funding organizations. In contrast to related work, this problem is solved by considering results from literature concerning the application based technological relationships and by creating a new approach that is based on latent semantic indexing (LSI) as semantic text classification algorithm. Technologies that occur together in the process of creating an application are grouped in classes, semantic textual patterns are identified as representative for each class, and projects are assigned to one of these classes. This enables the assignment of each project to all technologies semantically grouped by use of LSI. This approach is evaluated using the example of defense and security based technological research. This is because the growing importance of this application field leads to an increasing number of research projects and to the appearance of many new technologies.

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Section: Semantic Approachesmentioning

confidence: 99%

Technology classification with latent semantic indexing

Thorleuchter

Poel

2013

Expert Systems with Applications

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“…It uses singular value decomposition (SVD) as a mathematical technique from algebra to discover latent, underlying patterns within a collection of unstructured texts [17], [18]. The patterns consist of several terms that are semantically related.…”

Section: Latent Semantic Indexingmentioning

confidence: 99%

Linguistic Feature Classifying and Tracing

Moohebat

Raj

Thorleuchter

et al. 2017

MJCS

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We investigate the identification and analysis of linguistic (lexico-grammatical) features that are characteristically used by articles of a specific year of publication. Linguistic features differ from shallow features because they represent authors' lexico-grammatical writing styles and do not consider well-known bag-of-words model. Current literature focusses on shallow features rather than on linguistic features and existing methods for identifying linguistic features use well-known knowledge-structure based approaches. In contrast to this, we advance these existing methods by applying semantic clustering instead of using knowledge-structure based approaches. For evaluation purpose, a linguistic feature-based prediction model is built to enable an automated assignment of articles to their years of publication. In a case study, the proposed methodology is applied to articles of the Springer book series 'Communications in Computer and Information Science' published from 2009 to 2013. The Case study results show the feasibility of the proposed approach as compared to frequently used baseline.Keywords: Scientific articles, Linguistic features, Latent semantic indexing, Text Mining. INTRODUCTIONWe investigate the occurrence of linguistic (lexico-grammatical) features in articles to show that they can be used for assigning articles to their years of publication. The Literature shows related approaches that can be used to assign articles to a pre-defined class. A domain-specific vocabulary (key words) is often used for this classification task. Different domains can be well distinguished by the distribution of specific key words as shown by existing bag-of-words approaches [1]- [5]. Further, trend analysis and bibliometric research also show that key word distributions can be used to identify a time period [6]. They trace topic changes over time within a domain. Thus, these approaches can estimate an article's publication year based on the used topics.The approaches as mentioned above are based on shallow (bag-of-words) features. They are in contrast to linguistic features such as specific word class distributions that indicate authors' lexico-grammatical writing styles. Literature also shows the possibilities of using linguistic features for classification. [7] investigate the impact of linguistic features on different scientific disciplines and on different points in time. A further approach uses linguistic features for spam detection [8]. Both approaches are based on systemic functional linguistics, in which a knowledge-structure based classifier (e.g. support vector machine) is used.We provide a new approach that identifies articles' linguistic features and that investigates their usage at different points in time. In contrast to previous work, clustering is used instead of classification. Text classification assigns a text to the given pre-defined classes. Classes are normally defined in a way that they cover all known linguistic features that are expected to occur within the given texts. Text clusteri...

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“…Ontologies have been widely used as the background knowledge (i.e., knowledge bases) in a variety of text mining and knowledge discovery tasks such as text clustering [13], [14], [15], text classification [16], [17], [18], word sense disambiguation [19], [20], [21], and others. See [22] for a comprehensive review of Semantic Web in data mining and knowledge discovery.…”

Section: A Ontologiesmentioning

confidence: 99%

A Knowledge-based Topic Modeling Approach for Automatic Topic Labeling

Allahyari¹,

Pouriyeh²,

Kochut³

et al. 2017

ijacsa

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Abstract-Probabilistic topic models, which aim to discover latent topics in text corpora define each document as a multinomial distributions over topics and each topic as a multinomial distributions over words. Although, humans can infer a proper label for each topic by looking at top representative words of the topic but, it is not applicable for machines. Automatic Topic Labeling techniques try to address the problem. The ultimate goal of topic labeling techniques are to assign interpretable labels for the learned topics. In this paper, we are taking concepts of ontology into consideration instead of words alone to improve the quality of generated labels for each topic. Our work is different in comparison with the previous efforts in this area, where topics are usually represented with a batch of selected words from topics. We have highlighted some aspects of our approach including: 1) we have incorporated ontology concepts with statistical topic modeling in a unified framework, where each topic is a multinomial probability distribution over the concepts and each concept is represented as a distribution over words; and 2) a topic labeling model according to the meaning of the concepts of the ontology included in the learned topics. The best topic labels are selected with respect to the semantic similarity of the concepts and their ontological categorizations. We demonstrate the effectiveness of considering ontological concepts as richer aspects between topics and words by comprehensive experiments on two different data sets. In another word, representing topics via ontological concepts shows an effective way for generating descriptive and representative labels for the discovered topics.

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A semantic term weighting scheme for text categorization

Cited by 83 publications

References 12 publications

Technology classification with latent semantic indexing

Technology classification with latent semantic indexing

Linguistic Feature Classifying and Tracing

A Knowledge-based Topic Modeling Approach for Automatic Topic Labeling

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