An ontology-based approach to auto-tagging articles

Sriharee, Gridaphat

doi:10.1007/s40595-014-0033-6

Cited by 14 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In approach adopted in [5], the desired accuracy for all types of articles was not achieved. In ontology based approach in [6], a limited number of predefined tags are assigned to the articles, which does not give the most accurate results. All the aforementioned methods have been used for articles in English, which is comparatively easier since English words are separated by space.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Tagging of Chinese Articles

Kathait¹,

Tiwari²

2017

IJCA

View full text Add to dashboard Cite

Large amount of insights can be drawn from the articles that are published online. Instead of manually reading all the articles and assigning relevant tags to them satisfying the content, it will be highly efficient if there exists an automated process for performing the task. In this paper, an unsupervised approach for the automated tagging of articles in Chinese language has been implemented. The input is an article and output is the tags to that article. The major challenge is the segmentation of the Chinese characters, which do not make use of separators unlike the English characters. To overcome this, different approaches are combined together in order to get accurate results. Efficient tagging of articles is required, which can be used for many applications in the analysis, one of which is in Recommendation Engine. The tagging process should consider all the aspects of the article and assign the most relevant tags accordingly. The proposed algorithm was implemented for a Chinese Publication House and relevant tags were assigned to its articles of different categories. At the end of the project, the results were manually checked for, in a corpus of 10000 Chinese articles, which reflected the attainment of overall accuracy of around 85%, greater than that obtained through different traditional methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Unsupervised Tagging of Chinese Articles

Kathait¹,

Tiwari²

2017

IJCA

View full text Add to dashboard Cite

show abstract

“…In fact, they integrate ontologies in image processing algorithms in order to detect their semantics. Moreover, Sriharee (2014) used ontology to propose an auto-tagging articles methodology. The tagging is based on an ontology in order to use articles' meaning.…”

Section: Ijicc 92mentioning

confidence: 99%

Exogenous approach to improve topic segmentation

Naili

Chaïbi

Ghezala

2016

International Journal of Intelligent Computing and Cybernetics

View full text Add to dashboard Cite

Purpose – Topic segmentation is one of the active research fields in natural language processing. Also, many topic segmenters have been proposed. However, the current challenge of researchers is the improvement of these segmenters by using external resources. Therefore, the purpose of this paper is to integrate study and evaluate a new external semantic resource in topic segmentation. Design/methodology/approach – New topic segmenters (TSS-Onto and TSB-Onto) are proposed based on the two well-known segmenters C99 and TextTiling. The proposed segmenters integrate semantic knowledge to the segmentation process by using a domain ontology as an external resource. Subsequently, an evaluation is made to study the effect of this resource on the quality of topic segmentation along with a comparative study with related works. Findings – Based on this study, the authors showed that adding semantic knowledge, which is extracted from a domain ontology, improves the quality of topic segmentation. Moreover, TSS-Ont outperforms TSB-Ont in terms of quality of topic segmentation. Research limitations/implications – The main limitation of this study is the used test corpus for the evaluation which is not a benchmark. However, we used a collection of scientific papers from well-known digital libraries (ArXiv and ACM). Practical implications – The proposed topic segmenters can be useful in different NLP applications such as information retrieval and text summarizing. Originality/value – The primary original contribution of this paper is the improvement of topic segmentation based on semantic knowledge. This knowledge is extracted from an ontological external resource.

show abstract

“…Linking terms to external knowledge resources provides several benefits such as increased discoverability and accessibility, and hence the utility of information. Furthermore, it can be useful to add semantics to documents and hence allow machines to process documents in an intelligent manner [2].…”

Section: Introductionmentioning

confidence: 99%

Automatic Linking of Short Arabic Texts to Wikipedia Articles

Fayad¹,

AlAgha²

2016

JSW

View full text Add to dashboard Cite

Abstract:Given the enormous amount of unstructured texts available on the Web, there has been an emerging need to increase discoverability of and accessibility to these texts. One of the proposed solutions is to annotate texts with information extracted from background knowledge. Wikipedia, the free encyclopedia, has been recently exploited as a background knowledge to annotate text with complementary information. Given any piece of text, the main challenge is how to determine the most relevant information from Wikipedia with the least effort and time. While Wikipedia-based annotation has mainly targeted the English and Latin versions of Wikipedia, little effort has been devoted to annotate Arabic text using the Arabic version of Wikipedia. In addition, the annotation of short text presents further challenges due to the inability to apply statistical or machine learning techniques that are commonly used with long text. This work proposes an approach for automatic linking of Arabic short texts to articles drawn from Wikipedia. It reports on the several challenges associated with the design and implementation of the linking approach including the processing of the Wikipedia's enormous content, the mapping of texts to Wikipedia articles, the problem of article disambiguation, and the time efficiency. The proposed approach was tested on a dataset of 100 short texts gathered from online Arabic articles. The annotations generated by the approach were compared with the annotations generated by two human subjects. The approach achieved 71.79% accuracy, 74.70% average precision, and 82.63 % average recall. A thorough analysis and discussion of the evaluation results are also presented to address the limitations, strengths as well as recommendations for future improvements.

show abstract

An ontology-based approach to auto-tagging articles

Cited by 14 publications

References 7 publications

Unsupervised Tagging of Chinese Articles

Unsupervised Tagging of Chinese Articles

Exogenous approach to improve topic segmentation

Automatic Linking of Short Arabic Texts to Wikipedia Articles

Contact Info

Product

Resources

About