Short text clustering by finding core terms

Ni, Xingliang; Quan, Xiaojun; Lu, Zhi; Wenyin, Liu; Hua, Bin

doi:10.1007/s10115-010-0299-7

Cited by 50 publications

(25 citation statements)

References 33 publications

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“…Since short texts have limited information, extracting core terms in sentence is crucial. Ni et al [11] enhance similarity computation between core terms.…”

Section: B Short Text Understandingmentioning

confidence: 99%

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Consensus Similarity Measure for Short Text Clustering

Shin

Ahn

Jeon

et al. 2015

2015 26th International Workshop on Database and Expert Systems Applications (DEXA)

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Measuring semantic similarity between short texts is challenging because the meaning of short texts may vary dramatically even by a few words due to their limited lengths. In this paper, we propose a novel similarity measure for terms that allows better clustering performance than the state-of-the-art method. To achieve such performance, we incorporate knowledge-based and corpus-based term similarity measures in order to exploit advantages of both approaches. We apply our method to a dialog-utterance dataset, which consists of short dialog texts. Empirical study shows that the proposed method outperforms one of the state-of-the-art clustering algorithms for short text clustering.

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“…Since short texts have limited information, extracting core terms in sentence is crucial. Ni et al [11] enhance similarity computation between core terms.…”

Section: B Short Text Understandingmentioning

confidence: 99%

“…TABLE IV shows the statistics of the clusters. We use clustering evaluation measure mentioned in [11]. This TABLE V contains a sample of rated similarities from human and computed similarities from our method respectively.…”

Section: A Experimental Setupmentioning

confidence: 99%

Consensus Similarity Measure for Short Text Clustering

Shin

Ahn

Jeon

et al. 2015

2015 26th International Workshop on Database and Expert Systems Applications (DEXA)

View full text Add to dashboard Cite

show abstract

“…The combination of NMF and Test or theory provides effective results. Many document clustering algorithms are based on term frequency Kumar and Srinathan (2009;Luo et al 2009;Ni et al 2010). Several researchers have proposed clustering based on synonyms and hypernyms Bharathi and Vengatesan (2012;Pessiot et al 2010;Li et al 2008;Danushka et al 2011;Kaiser et al 2009;Baghel and Dhir 2010).…”

Section: Related Workmentioning

confidence: 99%

Correlated concept based dynamic document clustering algorithms for newsgroups and scientific literature

Jayabharathy

Kanmani

2014

Decis. Anal.

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“…Kim et al (2012) used Core-Topic-based Clustering to analyze tweets of particular dramas and extracted significant topics [9]. (Ni et al, 2011) extracted core words from Twitter to suggest movie contexts frequently referred by users and clustered similar tweets following topics [10]. …”

Section: Twitter Concepts and Featuresmentioning

confidence: 99%

Predicting the popularity of TV-show through text mining of tweets: A Drama Case in South Korea

Kim¹,

Kim²,

Choi³

2016

Journal of Internet Computing and Services

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This paper presents a workflow validation method for data-intensive graphical workflow models using real-time workflow tracing mode on data-intensiveworkflow designer. In order to model and validate workflows, we try to divide as modes have editable mode and tracingmode on data-intensiveworkflow designer. We could design data-intensive workflow using drag and drop in editable-mode, otherwise we could not design but view and trace workflow model in tracing mode. We would like to focus on tracing-mode for workflow validation, and describe how to use workflow tracing on data-intensive workflow model designer. Especially, it is support data centered operation about control logics and exchange variables on workflow runtime for workflow tracing.

show abstract

Short text clustering by finding core terms

Cited by 50 publications

References 33 publications

Consensus Similarity Measure for Short Text Clustering

Consensus Similarity Measure for Short Text Clustering

Correlated concept based dynamic document clustering algorithms for newsgroups and scientific literature

Predicting the popularity of TV-show through text mining of tweets: A Drama Case in South Korea

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