The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags

Eda, Takeharu; Yoshikawa, Masatoshi; Uchiyama, Takeshi; Uchiyama, Tadasu

doi:10.1007/s11280-009-0069-1

Cited by 28 publications

(19 citation statements)

References 26 publications

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“…Agglomerative clustering algorithm, Asymmetric hierarchical clustering, Hierarchical divisive clustering algorithms, Probabilistic Latent Semantic Indexing (PLSI) and UserCategorize tag (UCTag) have been tested and proposed by [4,33,46,101] respectively in order to make tag clusters. However, some clusters produced may be too large if utilized for navigation and for that, removing unpopular tags before clustering can be useful.…”

Section: Clusteringmentioning

confidence: 99%

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Semantics discovery in social tagging systems: A review

Jabeen

Khusro

Majid

et al. 2014

Multimed Tools Appl

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Web 2.0 has brought many collaborative and novel applications which transformed the web as a medium and resulted in its exponential growth. Tagging systems are one of these killer applications. Tags are in free-form but represent the link between objective information and users' cognitive information. However, tags have ambiguity problem reducing precision. Hence search and retrieval pose a challenge on folksonomy systems which have flat, unstructured, non-hierarchical organization with unsupervised vocabulary. We present a brief survey of different approaches for adding semantics in folksonomies thus bringing structure and precision in search and navigation. We did comparative analysis to estimate the significance of each source of semantics. Then, we have categorized the approaches in a systematic way and summarized the feature set support. Based on the survey we end up with recommendations. Our survey and conclusion will prove to be relevant and beneficial for engineers and designers aiming to design and maintain well structured folksonomy with precise search and navigation results.

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Section: Clusteringmentioning

confidence: 99%

“…Eda et al [33] used folksonomy triples to organize tags in generalized and specialized relationships. Using Probabilistic Latent Semantic Indexing (PLSI), they distinguished between subjective and objective tags and then arranged the objective ones into a hierarchy in a Directed Acyclic Graph.…”

Section: Hierarchical /Tag Taxonomymentioning

confidence: 99%

Semantics discovery in social tagging systems: A review

Jabeen

Khusro

Majid

et al. 2014

Multimed Tools Appl

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“…Bao et al explored the application of tag propagation processing for later web mining in [3]. In [7,10,20], latent feature based or user learning methods were used to discover latent concept hierarchy in the tag space. Our bursty tagging event detection is related to these attempts in the area of social tagging.…”

Section: Related Workmentioning

confidence: 99%

Bursty event detection from collaborative tags

et al. 2011

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Collaborative tagging have emerged as a ubiquitous way to annotate and organize online resources. As a kind of descriptive keyword, large amount of tags are created and associated to multiple types of resources, e.g., web pages, photos, videos and tweets. Users' tagging actions over time reflect their changing interests. Monitoring and analyzing the temporal patterns of tags can provide important insights to trace hot topics on the web. Existing work focuses on deriving temporal patterns for individual tags. However, there exist remarkable correlations among tags assigned to online resources. In this paper, we propose a new approach to detect bursty tagging event, which captures the relations among a group of correlated tags where the tags are either bursty or associated with bursty tag co-occurrence. This kind of bursty tagging event generally corresponds to a real life event. It profiles the events with more representative and comprehensible clues. The proposed approach is divided into three stages. We exploit the sliding time intervals to extract bursty features as the first step, and then adopt graph clustering techniques to group bursty features into meaningful bursty events. We discuss the choice of similarity and granularity for event detection. After that, we further utilize an automatically generated tag taxonomy to organize bursty events to facilitate the burst oriented 172 World Wide Web (2012) 15:171-195 navigation and analysis. The experimental study on a large real data set demonstrates the superiority of our new approach.

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“…The LDA-based approach usually delivers highly competitive performance on a number of natural language processing tasks including document classification, partly due to its nature of embedding documents in the low-dimensional semantic (topic) space [8].…”

Section: Introductionmentioning

confidence: 99%

Classification of e-government documents based on cooperative expression of word vectors

Fu¹,

Líu²,

Wei³

2017

AIP Conference Proceedings

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Abstract. The effective document classification is a powerful technique to deal with the huge amount of e-government documents automatically instead of accomplishing them manually. The word-to-vector (word2vec) model, which converts semantic word into low-dimensional vectors, could be successfully employed to classify the e-government documents. In this paper, we propose the cooperative expressions of word vector (Co-word-vector), whose multi-granularity of integration explores the possibility of modeling documents in the semantic space. Meanwhile, we also aim to improve the weighted continuous bag of words model based on word2vec model and distributed representation of topic-words based on LDA model. Furthermore, combining the two levels of word representation, performance result shows that our proposed method on the e-government document classification outperform than the traditional method.

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The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags

Cited by 28 publications

References 26 publications

Semantics discovery in social tagging systems: A review

Semantics discovery in social tagging systems: A review

Bursty event detection from collaborative tags

Classification of e-government documents based on cooperative expression of word vectors

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