Semantic, Hierarchical, Online Clustering of Web Search Results

Zhang, Dell; Dong, Yuan

doi:10.1007/978-3-540-24655-8_8

Cited by 129 publications

(58 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our work also share some commonalities with clustering and summarizing web search results [39,22,55,56,51,49,11]. Their methods try to group the search results into clusters and provide easy access and browsing ways for user to get information.…”

Section: Hierarchy Generationmentioning

confidence: 94%

An integrated system for building enterprise taxonomies

Zhang

Liu

et al. 2007

Inf Retrieval

View full text Add to dashboard Cite

Although considerable research has been conducted in the field of hierarchical text categorization, little has been done on automatically collecting labeled corpus for building hierarchical taxonomies. In this paper, we propose an automatic method of collecting training samples to build hierarchical taxonomies. In our method, the category node is initially defined by some keywords, the web search engine is then used to construct a small set of labeled documents, and a topic tracking algorithm with keyword-based content normalization is applied to enlarge the training corpus on the basis of the seed documents. We also design a method to check the consistency of the collected corpus. The above steps produce a flat category structure 1 which contains all the categories for building the hierarchical taxonomy.Next, linear discriminant projection approach is utilized to construct more meaningful intermediate levels of hierarchies in the generated flat set of categories. Experimental results show that the training corpus is good enough for statistical classification methods.

show abstract

Section: Hierarchy Generationmentioning

confidence: 94%

An integrated system for building enterprise taxonomies

Zhang

Liu

et al. 2007

Inf Retrieval

View full text Add to dashboard Cite

show abstract

“…Zhang et al [24] present a Chinese document clustering method using maximal substrings. However, the authors give no quantitative evaluation.…”

Section: Previous Workmentioning

confidence: 99%

Clustering Documents with Maximal Substrings

Masada

Takasu

Shibata

et al. 2012

Enterprise Information Systems

View full text Add to dashboard Cite

Abstract. This paper provides experimental results showing that we can use maximal substrings as elementary building blocks of documents in place of the words extracted by a current state-of-the-art supervised word extraction. Maximal substrings are defined as the substrings each giving a smaller number of occurrences even by appending only one character to its head or tail. The main feature of maximal substrings is that they can be extracted quite efficiently in an unsupervised manner. We extract maximal substrings from a document set and represent each document as a bag of maximal substrings. We also obtain a bag of words representation by using a state-of-the-art supervised word extraction over the same document set. We then apply the same document clustering method to both representations and obtain two clustering results for a comparison of their quality. We adopt a Bayesian document clustering based on Dirichlet compound multinomials for avoiding overfitting. Our experiment shows that the clustering quality achieved with maximal substrings is acceptable enough to use them in place of the words extracted by a supervised word extraction.

show abstract

“…Summarizing, clustering over full text is not appropriate for a (Meta) WSE since full text may not be available or too expensive to process. Other extensions of STC for oriental languages and for cases where external resources are available are described in [28,21].…”

Section: Related Workmentioning

confidence: 99%

STC+ and NM-STC: Two Novel Online Results Clustering Methods for Web Searching

Kopidaki

Papadakos

Tzitzikas

2009

Web Information Systems Engineering - WISE 2009

View full text Add to dashboard Cite

Abstract. Results clustering in Web Searching is useful for providing users with overviews of the results and thus allowing them to restrict their focus to the desired parts. However, the task of deriving singleword or multiple-word names for the clusters (usually referred as cluster labeling) is difficult, because they have to be syntactically correct and predictive. Moreover efficiency is an important requirement since results clustering is an online task. Suffix Tree Clustering (STC) is a clustering technique where search results (mainly snippets) can be clustered fast (in linear time), incrementally, and each cluster is labeled with a phrase. In this paper we introduce: (a) a variation of the STC, called STC+, with a scoring formula that favors phrases that occur in document titles and differs in the way base clusters are merged, and (b) a novel algorithm called NM-STC that results in hierarchically organized clusters. The comparative user evaluation showed that both STC+ and NM-STC are significantly more preferred than STC, and that NM-STC is about two times faster than STC and STC+.

show abstract

Semantic, Hierarchical, Online Clustering of Web Search Results

Cited by 129 publications

References 14 publications

An integrated system for building enterprise taxonomies

An integrated system for building enterprise taxonomies

Clustering Documents with Maximal Substrings

STC+ and NM-STC: Two Novel Online Results Clustering Methods for Web Searching

Contact Info

Product

Resources

About