A survey of Web clustering engines

Carpineto, Claudio; Osiński, Stanisław; Romano, Giovanni; Weiss, Dawid

doi:10.1145/1541880.1541884

Cited by 321 publications

(267 citation statements)

References 103 publications

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“…Reference [9] gave a brief overview of the document clustering research and the developments in this field. Reference [10] …”

Section: Document Clustering For English and Other Languagesmentioning

confidence: 99%

Big Data Categorization for Arabic Text Using Latent Semantic Indexing and Clustering

2016

International Conference on Engineering Technologies and Big Data Analytics (ETBDA’2016) Jan. 21-22, 2016 Bangkok (Thailand)

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Abstract-Documents categorization is an important field in the area of natural language processing. In this paper, we propose using Latent Semantic Indexing (LSI), singular value decomposing (SVD) method, and clustering techniques to group similar unlabeled document into pre-specified number of topics. The generated groups are then categorized using a suitable label. For clustering, we used Expectation-Maximization (EM), Self-Organizing Map (SOM), and K-Means algorithms. In our experiments, we use a corpus that contains 1000 documents of ten topics (100 document for each topic. The results show that using LSI and clustering techniques for Arabic text categorization achieves good performance. The results show that EM clustering method outperforms other investigated clustering methods with an average categorization accuracy of 89%.

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“…Reference [9] gave a brief overview of the document clustering research and the developments in this field. Reference [10] …”

Section: Document Clustering For English and Other Languagesmentioning

confidence: 99%

Big Data Categorization for Arabic Text Using Latent Semantic Indexing and Clustering

2016

International Conference on Engineering Technologies and Big Data Analytics (ETBDA’2016) Jan. 21-22, 2016 Bangkok (Thailand)

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“…At the root of the clustering approach lies van Rijsbergen's [32] cluster hypothesis: "closely associated documents tend to be relevant to the same requests", whereas documents concerning different meanings of the input query are expected to belong to different clusters. Approaches to search result clustering can be classified as data-centric or description-centric [7]. The former focus more on the problem of data clustering than on presenting the results to the user.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, Web clustering engines [7] have been proposed as a solution to the issue of lexical ambiguity in Web Information Retrieval. These systems group search results, by providing a cluster for each specific aspect (i.e., meaning) of the input query.…”

Section: Introductionmentioning

confidence: 99%

Clustering Web Search Results with Maximum Spanning Trees

Marco

Navigli

2011

AI*IA 2011: Artificial Intelligence Around Man and Beyond

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Abstract. We present a novel method for clustering Web search results based on Word Sense Induction. First, we acquire the meanings of a query by means of a graph-based clustering algorithm that calculates the maximum spanning tree of the co-occurrence graph of the query. Then we cluster the search results based on their semantic similarity to the induced word senses. We show that our approach improves classical search result clustering methods in terms of both clustering quality and degree of diversification.

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“…There has been significant work on clustering and classification of search results (Carpineto et al, 2009;Chen and Dumais, 2000;Zeng et al, 2004;Zhu et al, 2008). Our interest is specific to how previous research ranked classes.…”

Section: Related Workmentioning

confidence: 99%

“…Not only might these labels be unfamiliar to a user, but the same topic area may be assigned different labels depending on the documents in the group. A recent survey of clustering of search results can be found in (Carpineto et al, 2009). …”

Section: Introductionmentioning

confidence: 99%

Ranking Classes of Search Engine Results

Zhu

Levene

Cox

2010

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

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Abstract:Ranking search results is an ongoing research topic in information retrieval. The traditional models are the vector space, probabilistic and language models, and more recently machine learning has been deployed in an effort to learn how to rank search results. Categorization of search results has also been studied as a means to organize the results, and hence to improve users search experience. However there is little research to-date on ranking categories of results in comparison to ranking the results themselves.In this paper, we propose a probabilistic ranking model that includes categories in addition to a ranked results list, and derive six ranking methods from the model. These ranking methods utilize the following features: the class probability distribution based on query classification, the lowest ranked document within each class and the class size. An empirical study was carried out to compare these methods with the traditional ranked-list approach in terms of rank positions of click-through documents and experimental results show that there is no simpler winner in all cases. Better performance is attained by class size or a combination of the class probability distribution of the queries and the rank of the document with the lowest list rank within the class.

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A survey of Web clustering engines

Cited by 321 publications

References 103 publications

Big Data Categorization for Arabic Text Using Latent Semantic Indexing and Clustering

Big Data Categorization for Arabic Text Using Latent Semantic Indexing and Clustering

Clustering Web Search Results with Maximum Spanning Trees

Ranking Classes of Search Engine Results

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