W-kmeans: Clustering News Articles Using WordNet

Bouras, Christos; Tsogkas, Vassilis

doi:10.1007/978-3-642-15393-8_43

Cited by 16 publications

(7 citation statements)

References 10 publications

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“…Web data clustering researchers Bouras and Tsogkas (2010) proposed an enhanced model based on the standard k-means algorithm using the external information extracted from WordNet hypernyms in a twofold manner: enriching the "bag of words" used prior to the clustering process and assisting the label generation procedure following it.…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

Deposition of 137Cs and precipitation distribution in Vojvodina, Northern Serbia after the Chernobyl accident

et al. 2021

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Section: Literature Review and Backgroundmentioning

confidence: 99%

Deposition of 137Cs and precipitation distribution in Vojvodina, Northern Serbia after the Chernobyl accident

et al. 2021

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“…To determine the relationship between OD and Facebook articles, the OD should first be clustered. Bouras and Tsogkas 43 propose classification and clustering mainly for new articles using WordNet, a large English lexical database, and a K‐means clustering method for implementation. Taiwanese Facebook users mostly post articles and leave messages in traditional Chinese.…”

Section: Related Workmentioning

confidence: 99%

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Hsu

Lin

2020

Softw Pract Exp

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Summary Open government data (OGD) is a type of trusted information that can be used to verify the correctness of information on social platforms. Finding interesting OGD to serve personalized needs to facilitate the development of social platforms is a challenging research topic. This study explores how to link the Taiwanese government's open data platform with Facebook and how to recommend related OGD. First, an integrated machine learning with semantic web into cloud computing framework is defined. Next, the linked data query platform (LDQP) is developed to validate its feasibility. The LDQP provides a graphical approach for personal query and links with related Facebook fan pages. LDQP automatically finds highly relevant OGD based on recent topics that users are following on Facebook when users login to Facebook via the LDQP. In this way, the LDQP query result can be dynamically adjusted and graphically displayed according to user's Facebook operations.

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“…where k 1 expresses the impact of each particular keyword i that appears in the article's body, given its DATA2013-2ndInternationalConferenceonDataManagementTechnologiesandApplications relative frequency compared with the total number of appearances in the database (tf-idf), k 2 expresses the impact of keywords appearing also in the article's title and factors k 3 and k 4 express the impact of the categorization and summarization subprocesses (as depicted in Figure 1) respectively (for a more in depth analysis of the factors k 1 -k 4 , we suggest that the user studies: Bouras and Tsogkas (2008)). The previously described weight was previously also utilized in our W-kmeans algorithm (Bouras and Tsogkas, 2010).…”

Section: N-gram Weighting Analysismentioning

confidence: 99%

“…In our previous work (Bouras and Tsogkas, 2010), we proposed a new clustering method, called W-kmeans, which improves the traditional k-means algorithm by enriching its input with WordNet hypernyms. The WordNet lexical reference system, organizes different linguistic relations into hierarchies/hypernyms (Is-a relation) and W-kmeans uses them as a preprocessing stage before the regular kmeans algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Keyword extraction, by using the vector space model, generates the term-frequency vector, describing each article as a 'bag of words' (words -frequencies vector) to the key information retrieval techniques that follow: article categorization, summarization and clustering. Previously, (Bouras and Tsogkas, 2010) we had successfully enhanced the efficiency of this 'bag of words' with the use of an external database, WordNet, in order to improve the results of the clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing News Articles Clustering using Word N-Grams

Bouras

Tsogkas

2013

Proceedings of the 2nd International Conference on Data Technologies and Applications

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In this work we explore the possible enhancement of the document clustering results, and in particular clustering of news articles from the web, when using word-based n-grams during the keyword extraction phase. We present and evaluate a weighting approach that combines clustering of news articles derived from the web using n-grams, extracted from the articles at an offline stage. We compared this technique with the single minded bag-of-words representation that our clustering algorithm, W-kmeans, previously used. Our experimentation revealed that via tuning of the weighting parameters between keyword and n-grams, as well as the n itself, a significant improvement regarding the clustering results metrics can be achieved. This reflects more coherent clusters and better overall clustering performance.

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W-kmeans: Clustering News Articles Using WordNet

Cited by 16 publications

References 10 publications

Deposition of 137Cs and precipitation distribution in Vojvodina, Northern Serbia after the Chernobyl accident

Deposition of 137Cs and precipitation distribution in Vojvodina, Northern Serbia after the Chernobyl accident

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Enhancing News Articles Clustering using Word N-Grams

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